<p>Distributed Generation will be used more frequently in tandem with electric vehicles in the upcoming years. In this study, we investigate the use of a multi-tasking evolutionary algorithm to load models for distributed generation and electric vehicles in distribution networks in order to make rating and placement decisions. The goal of this research is to reduce total actual power loss from a system perspective. The analysis considers several distributed generation possibilities for electric vehicles from this point of view. Plug-in hybrid electric vehicles, battery electric vehicles, fuel cell electric vehicles, and extended plug-in hybrid electric vehicles are all taken into consideration in this study. In distributed generations with electric vehicles, several constant impedance, constant current, and constant power load models are employed to optimize system performance indices. The real power loss indices improvement (4.13%), the reactive power loss indices improvement (2.10%), the short circuit current reduction (2.25%), the voltage deviation index improvement (4.71%), the real power distributed generations with penetration of electric vehicles improvement (6.52%), and the reactive power distributed generations with penetration of electric vehicles improvement (4.31%) are all examined in this study. Electric vehicle scheduling is used to compare the system performance of different distributed generating methods in distribution systems that use load models. Distributed generation with the proper mix and quantity of battery electric vehicles, extended plug-in hybrid electric vehicles, fuel cell electric vehicles, and plug-in hybrid electric vehicles improves system performance. The efficacy of the proposed methodology is evaluated on a 37-bus distribution system with various distributed generations of electric vehicles.</p>