This paper presents the Vulture algorithm, which addresses the challenge of positioning and sizing electric vehicle (EV) charging stations within a continuous search space. The algorithm combines the Particle Swarm Optimization (PSO) method with a k-nearest-neighbor heuristic. This algorithm may be applied to non-convex and non-linear problems with continuous and discrete search spaces. A sensitivity analysis was conducted to examine the impact of various assumptions and parameters on the solution, such that cost was minimized and charging is convenient for EV drivers. The Vulture algorithm was implemented in AIMMS and integrated with a user interface to explore and modify parameters, as well as view a cost breakdown and inspect station positions on a map. The algorithm provides a foundation for optimal EV charging station planning and can be extended with practical constraints and objectives suggested by urban planning practitioners.