The search for habitable locations beyond Earth has led to the discovery of TRAPPIST-1, a cool red dwarf star with seven exoplanets, four of which may support human life because of the presence of water. However, more materials are needed for sustainable living. The space explorer initiative, Space Optimization Competition(SpOC)( https://optimise.esa.int/challenges ) hosted by the European Space Agency(ESA)’s Advanced Concepts Team and GECCO 2024( https://gecco-2024.sigevo.org/HomePage ), is working on efficient solutions to transport materials from an asteroid belt to twelve robotic processing stations in the TRAPPIST-1 system, requiring an optimized delivery schedule for this transfer of asteroids. Delivery scheduling is a challenging task that requires several aspects to be optimized. A generic evolutionary algorithm, like Genetic Algorithms (GA), may have low fitness and sluggish convergence rates, according to the research. Nevertheless, the best outcomes can be obtained by combining it with a Local Search (LS). This method uses a Hybrid Genetic Algorithm (HGA) within a meta-heuristic framework, adapting Genetic Algorithm parameters (Adaptive GA) along with a repair technique for non-overlapping time windows, then A Local Search (LS) method is also applied to the results of the Adaptive GA. This study develops four sets of algorithms to determine the adaptive parameters for AGA. While LS techniques are not extensively utilized in this research, there is potential for future studies to explore and identify the most effective outcomes.