<p>As electric vehicle (EV) adoption continues to rise, power grids are challenged with providing fair and efficient energy distribution. In this paper, a fairness-aware genetic algorithm (GA) is presented for optimizing EV charging schedules under power and time constraints. The approach integrates a Least Laxity Ratio (LLR)-based fairness metric into the GA fitness function to balance total charging delay, cost, and service equity. Charging schedules are represented as ordered sequences of EVs, with scheduling decisions driven by a weighted multi-objective function. A realistic synthetic dataset modeled after commercial EV data is used to assess the approach comprising over 2,000 commercial EV charging sessions. Results demonstrate a 20% reduction in average delay and a significant improvement in fairness, as indicated by an increase in Jain’s index from 0.78 to 0.92, compared to traditional baselines such as First-Come-First-Served (FCFS) and greedy heuristics. As a result, the algorithm keeps passengers within the station capacity limits without raising the typical wait time. The analysis identifies the compromise grid operators should consider between deciding on cost-effective actions and fairness. While the suggested model supposes that vehicle arrival and departure times are known in advance and runs under a deterministic environment, it provides a basis for adaptive scheduling paradigms. Real-time scheduling with uncertainty, coupling with renewable energy sources, and scalability and security enhancements will be studied in future work. This study contributes a scalable and equitable solution for EV charging management aligned with the evolving requirements of smart grids.</p>