In recent years, researchers have applied different types of evolutionary algorithms (EAs) to solve multi-objective optimization problems [8; 9; 23]. However, since the conventional EAs are designed to be used for single-objective unconstrained optimization problems, they cannot be directly applied to solve constrained multi-objective optimization problems. To deal with the constraints, several different constraint handling techniques have been proposed in the literature, which can be broadly classified as: penalty functions, separate considerations of infeasible and feasible solutions and dealing with constraints using the MOO problem [24]. First method is the most widely employed technique to deal with constraints, where COP is transformed into the unconstrained optimization problem by introducing a penalty value term to the objective function, which is related to the constraints. The second method deals with the constraints in two stages. First, the algorithm optimizes constraints without taking into the account the objective functions. Then, in the second stage, when significant number of feasible solutions has been obtained the algorithms focuses on the optimization of objective functions. The newest technique, among the above-mentioned, treats the constraints of the MOO problem as the additional objectives, and therefore transforming the constrained optimization problem to unconstrained MOO problem. In recent years, several metaheuristic algorithms inspired by nature, such as Genetic Algorithm (GA) [10], particle swarm optimization (PSO) [25], differential evolution (DE) [26], firefly algorithm (FA) [27], gray wolf optimization (GWO) [28] etc. have been applied to solve a wide array of engineering optimization problems. Among them the GA, as a promising optimization method from the group of EAs has been widely applied to solve different SOO problems regarding the gearbox optimization, such as weight minimization, power loss minimization etc., [3; 4; 5]. Besides the GA, several papers have been published that deal with SOO of gear trains using the algorithms, such as DE, PSO and GWO [28; 29; 30]. Regarding the MOO problems in gearbox design, most of the papers outlined previously are relying on the well-known NSGA-II and other improved variations of this algorithm to solve this complex optimization problem [10; 13; 14]. Recently, several papers appear in the literature that deal with the MOO problems using the hybrid variants of the well-known EAs [8; 31]. Although a lot of work in the literature is devoted on developing more effective optimization algorithms and improving the performance of the existing ones, due to the no free lunch theorem of mathematical optimization the algorithm maximizing performance on one class of optimization problem will likely perform worse at others. The no free lunch theorem states that if no prior assumptions about the optimization problems can be made, averaging over all possible optimization problems, all optimization algorithms have equal performance [32], i.e., it is not possible to provide general-purpose universal optimization algorithm, rather only to provide a strategy that can outperform another on the specific group of optimization problems under consideration. Among different EAs, DE emerged as the efficient algorithm which can successfully obtain global optimum of multimodal optimization problems. This algorithm has been initially proposed by [PERSON] and [PERSON] [26] to solve real-valued unconstrained single-objective optimization problems. However, due to its simple structure and easy implementation it has since been expanded to solve mixed integer, constrained and multi-objective optimization problems [2; 33]. The performance of DE algorithm depends in large measure on the appropriate choice of its main control parameters, such as scale factor \(F\), crossover rate \(CR\) and population size \(N_{P}\)[34].