The aircraft speed control system plays a critical role in maintaining a designated speed during the cruise phase of flight, ensuring alignment with predefined mission objectives. Maintaining optimal cruise speed is vital for efficient fuel usage, engine performance, and overall mission success. This study presents a multi-objective optimization approach aimed at generating a set of optimal speeds that simultaneously maximize both flight range and endurance. By providing a diverse set of trade-off solutions, the control system gains flexibility in selecting the most appropriate reference speed during flight, considering operational constraints such as fuel limitations, time restrictions, navigation requirements, or emergency rerouting scenarios.The core motivation behind this research lies in addressing complex mission profiles, including emergency operations where extended range is necessary to reach a distant alternate airport while maintaining sufficient endurance to ensure safety and stability. To achieve this, a computational optimization method—specifically a multi-objective genetic algorithm (MOGA)—is employed to evaluate the trade-offs between competing objectives. The results are further validated by comparison with an alternative method, such as the multi-objective particle swarm optimization (MOPSO), to improve reliability.A conventional PID controller is then used to regulate the aircraft’s speed, ensuring it converges in real time to one of the optimal values derived from offline optimization. Simulation results confirm that these precomputed optimal speeds can serve as reference values for real-time control, improving the adaptability and performance of both piloted and autonomous aircraft. This framework bridges the gap between offline planning and online execution, enhancing efficiency in critical missions.