This study investigates the essential role of satellite constellations within the rapidly expanding space economy, emphasizing their increasing contribution to global connectivity, navigation, and economic development. As the demand for reliable space-based infrastructure accelerates, the design and optimization of efficient satellite constellations have become a critical engineering challenge. This research focuses on determining key orbital design parameters for satellite constellations, with particular emphasis on developing an optimized configuration specifically for the Tehran metropolitan region. To address the inherent complexity and high-dimensional nature of the constellation design problem, a hybrid optimization approach is introduced that combines the strengths of Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) techniques. The problem is formulated as a thirty-variable optimization task that incorporates both global and local search strategies. Initially, the GA explores the broad solution space to identify high-quality global optima. These solutions subsequently serve as initial conditions for a refined optimization process using the GA-PSO hybrid algorithm, which further improves solution quality and enhances computational efficiency. Through this two-stage optimization process, the objective function was improved by 610 seconds compared to the GA-only approach, demonstrating the effectiveness and superiority of the proposed hybrid method. The final outcome is the design of a ten-satellite LEO constellation optimized to provide maximum coverage over Tehran, achieving 17 hours of daily coverage within a 24-hour period. The findings underscore the significant potential of advanced computational optimization techniques for improving better satellite constellation design and contribute to the development of more efficient, reliable, and cost-effective space-based telecommunication solutions.