The objective of this research was to optimize plant delivery routes for a reforestation area in the Mexican highlands, ensuring efficient transport to planting polygons under specific logistical constraints. These constraints included the maximum number of plants that can be loaded into each truck and the required plant species distribution across polygons. This study presents a hybrid solution combining clustering algorithms and a greedy algorithm approach, which reduces the search space and minimizes computational complexity. The results demonstrate a significant reduction in the total number of routes, minimizing the distance traveled while meeting all logistical requirements. The proposed model is adaptable to any reforestation zone, making it a feasible solution to the challenges of reforestation logistics. The optimization proves effective in providing a practical, constraint-compliant solution for efficient route planning in large-scale reforestation efforts.