The rapid growth of urbanization and the rising demand for fast delivery services have challenged traditional distribution systems, leading to traffic congestion, increased costs, and environmental pollution. Problem definition: In this context, the use of quadcopters as an innovative tool for optimizing the distribution process has been proposed; however, technical, environmental, and regulatory limitations remain major obstacles to their practical deployment. Significance: Optimizing distribution with quadcopters can not only reduce pressure on road transport networks but also play a vital role in enhancing service quality and achieving smart cities. This study aims to design and develop a metaheuristic algorithm for optimizing quadcopter flight paths in District 1 of Tehran Municipality. Spatial data—including street networks, no-fly zones, distribution centers, and order locations—were collected and organized in a GIS environment. Three algorithms—Ant Colony Optimization (ACO), Genetic Algorithm (GA), and Particle Swarm Optimization (PSO)—were implemented and simulated in MATLAB. The results indicate that PSO outperforms the other two algorithms, reducing delivery time by an average of 35%, optimizing energy consumption by 28%, and demonstrating higher statistical stability under varying conditions. These outcomes highlight the effectiveness of quadcopters in improving the efficiency and sustainability of urban distribution systems.