<p>The rapid adoption of electric vehicles (EVs) requires an efficient and strategic placement of Electric Vehicle Charging Stations (EVCS) to meet growing demand and ensure user accessibility. This work presents an integrated decision-support approach that combines the Analytic Hierarchy Process (AHP) with a Genetic Algorithm (GA) to identify optimal EVCS locations. Five key criteria–traffic congestion, distance between charging stations and reference locations, availability of operational infrastructure, road network characteristics, and operational and management cost were selected based on a literature review and expert surveys. AHP was used to quantify the relative importance of each criterion, and Min-Max normalization was applied to standardize the data. To avoid spatial clustering and to improve balanced distribution, a distance-based scaling function was incorporated. The dataset was processed using ArcGIS and served as input for the GA, which iteratively evolved candidate solutions while satisfying domain-specific spatial constraints. The novelty of this work lies in the integration of ArcGIS with GA for spatially informed EVCS placement, combined with AHP-derived criteria weights to guide optimization according to expert priorities. This approach ensures that candidate locations are evaluated in a geospatially realistic and theoretically grounded manner, producing solutions that are both interpretable and aligned with planning objectives. The proposed GeoGA Framework provides a practical decision-support tool for state agencies, municipalities, and utility providers to design equitable and data-driven EV charging deployment strategies. A case study in rural Tennessee demonstrated the method’s effectiveness in identifying cost-effective and well-distributed charging station locations, highlighting its potential for real-world infrastructure planning.</p>