The pursuit of excellent performance in meta-heuristic algorithms has led to a myriad of extensive and profound research and achievements. Notably, many space mission planning problems are solved with the help of meta-heuristic algorithms, and relevant studies continue to appear. This paper introduces a hierarchical optimization frame in which two types of particles—B-particles and S-particles—synergistically search for the optima. Global exploration relies on B-particles, whose motional direction and step length are designed independently. S-particles are for fine local exploitation near the current best B-particle. Two specific algorithms are designed according to this frame. New variants of classical benchmark functions are used to better test the proposed algorithms. Furthermore, two spacecraft trajectory optimization problems, spacecraft multi-impulse orbit transfer and the pursuit-evasion game of two spacecraft, are employed to examine the applicability of the proposed algorithms. The simulation results indicate that the hierarchical optimization algorithms perform well on given trials and have great potential for space mission planning.