The rapid embracement of electric vehicles has heightened the demand for efficient and effective routing solutions, accounting for the limits of accessible charging facilities. This study addresses the challenge of planning EV routes with severe capacity limitations of charging points such that realistic, energy-conserving trips are kept and reduced waiting times due to a lack of chargers are minimized. We cast the problem as a constraint-based optimization model involving vehicle energy consumption, station charging demands, station capacities, and routing costs. A mixed-integer programming framework is constructed in order to model these interacting factors, and heuristic and metaheuristic approaches are recommended for large instances. Experimental performance on simulated city grids indicates that capacity constraints of infrastructure included in the model result in very different optimal routing patterns compared with standard energy-aware models, justifying coordinated route and charge planning. The findings highlight the relevance of capacity-aware optimization in making EV adoption scalable and offer insight into the strategies for infrastructure planning and real-time fleet management.