The strategic placement of green infrastructure is a key challenge in sustainable urban planning. This paper addresses the Green Covering Problem (GCP), formulated as a robust min-max-min optimization model. The model aims to minimize the total uncovered distance by considering the worst-case demand point within each residential area, a formulation that ensures equitable coverage but is NP-hard. To tackle this challenge, we propose a heuristic approach based on the Constriction Factor Particle Swarm Optimization (PSO) algorithm. Potential solutions, representing sets of tree locations, are encoded as particles that navigate a high-dimensional search space, with their stability managed by the constriction factor. Computational experiments on instances of varying scales and a real-world case study demonstrate the algorithm’s effectiveness in finding high-quality solutions. The results highlight the trade-offs between problem size, computational cost, and solution quality, proving the method to be a viable tool for decision-support systems in urban planning.