###### Abstract  
This paper investigates the effect of selected strategies of distributed energy resources (DER) on an energy cost function that optimizes the distribution of distributed energy resources for a mid-rise apartment building. This is achieved through comparing parameter optimization results for both a high-level and low-level optimizer, respectively. The optimization process is carried out using the following approach: (1) a two-objective function is constructed with one objective function similar to that of the high-level optimizer (DER-CAM); (2) an evolutionary algorithm (EA) with modified selection capability is used to optimize the two-objective function problem in (1) for four selected cases of DER utilization that were previously optimized in DER-CAM; and (3) the optimization results of the low-level optimizer are compared with the outcome of DER-CAM optimization for the four selected cases. This is done to establish the capability of DER-CAM as an effective tool for optimal distributed energy resource allocation. Results obtained reveal the effect of load shifting and solar photovoltaic (PV) panels with power exporting capability on the optimization of the cost function. The Pareto-based MOEA approach has also proved to be effective in observing the interactions between objective function parameters. Mean inverted generational distance (MIGD) values obtained over 10 runs for each of the four cases that were considered show that a DER combination of PV panel, battery storage, heat pump, and load shifting outperforms the other strategies in 70% of the total simulation runs. home electricity consumers also become producers using the smart grid (SG) concept [5; 6; 7] and the microgrids approach [8; 9]. Over the years, efficient energy dispatch management has become an important priority for distribution network operators. This is because they seek to minimize distribution system losses and the cost of maintenance of energy distribution equipment, while maximizing profits and customer satisfaction. This has been made possible through the evolution in distribution network architecture by the application of advances made in computer science and engineering, and also supervisory control and data acquisition (SCADA) systems [2]. These advancements have led to the development of the concept of smart power grids that are capable of adjusting the relevant power supply parameters based on changing demand patterns. A review of the key concepts of smart grids can be found in [PERSON] et al., [PERSON] et al. and [PERSON] [5; 6; 7]. Specifically, [PERSON] et al. [5] explore the optimization of distribution smart grids with distributed renewable generation using a novel approach based on complex network concepts [10] with evolutionary algorithms. The use of evolutionary algorithms and other computational intelligence techniques in energy can be found in [PERSON] et al., [PERSON] et al. and [PERSON] et al. [11; 12; 13]. Also, with the recent passage of laws in countries such as Spain, Germany, and the United Kingdom banning restrictions on private energy sourcing [14; 15], the demand is rising for specialized software to meet consumers' energy needs. Since the distribution side of a power supply system represents the downstream sector of the system, which links the generation sector with the final consumer, efficient and reliable power supply can only be guaranteed when the distribution system is optimally modeled. Also, the development of alternative energy sources has transformed energy consumers to 'prosumers', which means that they can now play a more active role in the utilization of electricity. Therefore, having a distribution network that is able to accommodate these changes would result in enormous benefits for both the supply and demand side of the energy distribution network. Optimization is the process of achieving a set objective with the least possible resources. This is important when it is difficult to determine which combination of variables yields the most viable outcome by simple observation.