The integration of renewable energy sources into modern power systems has increased significantly in recent years. Rising global temperatures, extreme climate events, and the growing demand for advanced transportation systems—driven by rapid societal and technological development—have encouraged nations worldwide to adopt electric vehicles (EVs) as a sustainable alternative. In this context, this paper proposes a novel Sliding Mode Maximum Power Point Tracking (MPPT) controller for photovoltaic (PV) systems operating under rapidly changing atmospheric conditions. The method enhances the conventional Perturb and Observe (P&O) algorithm by incorporating variable step size adjustment driven by optimally tuned Sliding Mode Controller (SLMC) gains obtained through a Genetic Algorithm (GA). Additionally, a PI-based grid current control scheme and an efficient EV charging station powered by the GA-optimized SLMC-based reconfigurable step-size P&O MPPT controller are developed and evaluated in MATLAB/Simulink. The primary contribution of this work is the improved tracking capability of the MPPT controller, achieving fast convergence to the maximum power point (MPP) with minimal oscillation, reduced overshoot, low ripple, and robust performance under rapidly fluctuating weather conditions—ensuring uninterrupted power delivery to the EV charging station. Comparative analysis demonstrates that the proposed system outperforms existing methods in the literature. Furthermore, the inclusion of grid integration ensures stable and continuous power supply even under uncertain environmental conditions.