This thesis addresses the optimization of on-demand satellite servicing in Sun-Synchronous Orbits through dynamic routing and refueling strategies. Traditional satellite operations, limited by finite onboard fuel, lead to premature mission termination. This research focuses on optimizing a refueling infrastructure comprising fuel stations, a service satellite, and client satellites, specifically for SunSynchronous Orbits. The algorithm is designed to efficiently manage fuel resources by determining the most effective servicing routes to minimize mission time and fuel consumption. A multi-objective optimization framework is developed to model these operations, utilizing a custom algorithm that refines transfer trajectories through high-precision numerical simulations that account for perturbations. Robustness analysis demonstrates that the algorithm can effectively handle uncertainties, consistently converging to the same optimal solutions across varying conditions. The developed algorithm efficiently identifies optimal servicing paths and is scalable to more complex missions involving multiple satellites and refueling stations.