In satellite constellation design, particularly for navigation and positioning applications, system performance and associated costs are among the key factors guiding the design process. In recent years, Low Earth Orbit (LEO) satellite constellations have attracted significant attention from researchers and industry professionals due to their ability to provide wide coverage, low latency, and relatively lower manufacturing and launch costs. In this context, the present study aims to develop a design tool for the simultaneous optimization of performance and cost in LEO based navigation constellations. The proposed tool is developed by linking MATLAB and STK software environments and employs the Multi-Objective Particle Swarm Optimization (MOPSO) algorithm to explore the design space efficiently. The main performance indicator considered in this study is the Geometric Dilution of Precision (GDOP) at the Earth's surface. Additionally, to estimate the total system cost, a combination of the USCM8 and SSCM cost models is utilized. The design parameters include the number of satellites, the number of orbital planes, Walker constellation configurations, orbital elements, and transmitter power. The developed design tool is capable of conducting multi-scenario evaluations to provide a comprehensive and detailed analysis of the trade-off between coverage accuracy and system cost. The simulation results demonstrate that the tool generates reliable and realistic solutions, making it a robust framework for the conceptual design of navigation satellite constellations. This study contributes to the integration of intelligent optimization methods with advanced space simulation tools and paves the way for more advanced research in the field of space system design.