Electric vehicles (EVs) play a crucial role in reducing greenhouse gas emissions and meeting climate targets. However, the increasing adoption of EVs presents significant challenges to the existing electrical grid, underscoring the necessity for efficient charging strategies. This paper explores the intricate task of scheduling EV charging at public stations, considering the vehicles’ arrival and departure times. EV drivers specify their charging needs before reaching the station. To maximize the station’s revenue, the scheduler strategically allocates chargers and manages power distribution, ensuring that power capacity and charger availability constraints are met. As customers may withdraw their demand if the station cannot meet their minimum required charging level, a revenue management strategy is used to prioritize loyal customers with consistent, long-term demands over random customers with uncertain future needs. The problem is initially formulated as a mixed-integer linear programming (MILP) model, which is solved using an exact solver. Additionally, a heuristic algorithm and a variable neighborhood search (VNS) metaheuristic are developed as alternative solution approaches. Simulation results demonstrate the effectiveness of the proposed methods, highlighting their capability to address the complexities of EV charging scheduling problems efficiently.