— In recent years, little work has been done over the joint reliability -redundancy allocation and in all of that, exponential failure and repairs are assumed. In this paper we focus on investigating the reliability -redundan cy allocation where the repair failure distributes are non - exponential. When failure/repair times follow a distribution other than exponential, analytical methods become too complicated or even useless. In these cases simulation methods like Mote Carlo and discrete event simulations are typically preferred, but they usually take relatively long time to estimate the availability of the system. This weakness gets more important when we want to use them in optimization algorithms. Here we offer a new discrete event computer simulation package which can estimate availability values in a reasonable time. Then we combine the mentioned simulation method with genetic algorithm to obtain optimal designs. These methods are illustrated with their application using some numerical examples. Keywords - Reliability Optimization; Non -Exponential; Multi -State; Reparability; Simulation; Multi -Objective . I. INTRODUCTION AND BACKGROUND Reliability Optimization and designing reliable systems with acceptable availability has always be en a very important task for managers and engineers and that is because of the close relationship between reliability/availability and other concepts like income, quality and safety ‎[1]. Many researchers have discussed the rel iability optimization problem with different assumptions and each from certain point of view . However, in general we can distinguish three different approaches for this problem: (i) reliability allocation, (ii) redundancy allocation, and (iii) joint reliab ility- redundancy allocation. The first type aims at determining the best reliability specification for all components in the system without making an y changes in system structure [2 -5]. While the second approach tries to determine the optimal number of redundant for each stage. This approach has been used by a number of papers such as [ 6-7]. ComponentReparability State Destribution Fig. 1 Characterestics triangle However, as explained in ‎[8] , each of these approac hes can only result in partial optimizations. Thus, Misra and Lobojevic first introduced the third approach by combining the previews allocation models. So this type of optimization utilizes the both options simultaneously, i.e.‎adjusting‎components’‎avail ability and determining the number of components in subsystems. For each of these allocation methodologies, components have three major characteristics, namely component state, reparability, and Failure/Repair distribution. We call this the‎ “characteristi cs‎ triangle”‎ ( Fig 1). The number of states available for the component forms the first side of this triangle. Each component has at least two possible states: perfect functioning and complete failure. Engineering system can usually take some medial states in addition to these basic states. Components with only two possible states are commonly called binary state and those with more than two states are called multi -state components ‎[1]. On the next side, we determine whether the component is repairable or not. Failure and repair www.iiec 2015.org distributions are the other important attribute of a component that is indicated on the last side of this triangle. In reliability theory, Exponential, nor mal, lognormal and weibull are the commonly use d life and repair distributions. Among all the possible combinations of these characteristics, authors have always focused on some specific and simplified assumptions. For instance, in joint reliability -redund ancy allocation, Misra and Lobojevic ‎[8] discussed binary state and repairable components. Since then, many papers were limited to those characteristics and except for very few papers like ‎[10], all the studies in binary state components neglected the repair op tion. Tian et.al. ‎[11], for the first time, considered multi -state components with this approach and in ‎[12] this problem was extended to include repairable components. Recently, Hamedani a nd Khor shidi [13 ] investigated the model formulated in ‎[12]using time value of money. The last two