It is frequently preferred to perform development processes to improve the results of optimization algorithms and increase their performance. Swarm-based metaheuristic optimization algorithms are frequently preferred due to their ease of application and fast results. In this study, the alpha wolf class, also called the wolf leader class in grey wolf optimization (GWO), was improved with chaotic Chebyshev map and named as chGWO. 7 of the standard test functions were used to evaluate the performance of chGWO and the findings were compared with the literature. Based on the comparisons of the algorithms in the literature, the chGWO algorithm gave good results in single-mode benchmark functions. Then, the improved algorithm was applied to the problem of optimum placement of electric vehicle charging stations (EVCSs) in the grid using the IEEE 33-bus test system. It gave better results than the classical GWO algorithm. It was seen that the improved chGWO was advanced and could be used in solving various engineering problems.