Over the past 20 years, the popularity of renewable energy has sharply increased due to environmental concerns. Integrating Distributed Generation (DG) and renewable energy sources, particularly through microgrids, into power distribution systems has become increasingly feasible. Simultaneously, there has been a notable surge in the adoption of electric vehicles (EVs), driven by environmental initiatives and their advantages over internal combustion engines. Consequently, the planning and management of microgrids within distribution networks have grown increasingly complex. To tackle these complexities, computational evolutionary algorithms have emerged as effective solutions. Among these algorithms, the Differential Evolution (DE) algorithm stands out for its speed and user-friendly simplicity. The proposed work analyzes Hybrid Differential Evolution (HDE) integrated with EV charging infrastructure for microgrid planning. The HDE algorithm combines the power of fuzzy logic and adaptive strategies within the DE framework to address the planning and optimization challenges of microgrids integrated with Electric Vehicle Charging Stations (EVCS). The paper gives insights into the effectiveness of the HDE algorithm in addressing the challenges related to the planning and operation of microgrids with EV charging stations in modern power systems. Furthermore, the optimization results are compared with those achieved using the DE algorithm.