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This study assesses the integration of renewable energy sources with grid-connected systems to power electric vehicle charging stations (EVCSs) in Riyadh, Kingdom of Saudi Arabia (KSA), emphasizing its significance for sustainable development. An environmentally friendly EVCS model and a novel metaheuristic optimization technique, the Giza Pyramids Construction Algorithm (GPCA) is employed, to determine the optimal sizing of system components. The objective is to ensure dependability, as determined by the lack of power supply probability (LPSP), while minimizing the levelized cost of energy (LCOE) and total net present cost (TNPC). We assess the efficacy of other algorithms in scaling system components by contrasting the performance of the GPCA with those of the Artificial Hummingbird Algorithm (AHA), Flower Pollination Algorithm (FPA), and Whale Optimization Algorithm (WOA). The study also examines the impact of climatic, financial, and technical factors on system costs, providing insights into the challenges, opportunities, and socio-economic factors influencing the deployment of EV infrastructure in KSA. Findings suggest that the optimal EVCS configuration is a solar photovoltaic/wind turbine (SPV/WT) grid-tied system, resulting in an LCOE of <inline-formula> <tex-math notation="LaTeX">${\$}$ </tex-math></inline-formula>0.0796/kWh, a TNPC of <inline-formula> <tex-math notation="LaTeX">${\$}$ </tex-math></inline-formula>102,042, and an annual CO2 emission reduction of 18,840 kg. The system achieves a 67.9% renewable penetration rate with 30.8% annual grid electricity purchase and 32.4% sale. Sensitivity analysis on renewable parameters is also conducted, demand charges, grid purchase prices, discount rates, and inflation rates to assess their impact on the system&#x2019;s economic variables. By identifying current constraints and potential strategies, our study aims to inform policy-making and investment decisions for expanding electric vehicle charging infrastructure, with broader implications for developing nations.