Abstract: To address the trade-off between stability and efficiency in airport foreign object debris cleaning tasks using a quadruped robot integrated with a robotic arm—where excessive arm motion speed amplifies joint impacts, destabilizing the quadruped platform, while overly slow motion compromises cleaning efficiency—this study proposed a robotic arm trajectory planning method based on an enhanced multi-objective particle swarm optimization algorithm. A 3-5-3 piecewise polynomial was first employed to formulate smooth joint trajectories. An optimization model was subsequently established with motion time and total joint impact as dual objectives, constrained by position, velocity, and acceleration limits. The IMOPSO algorithm resolved the multi-objective optimization problem, and a normalized weighting function converted the results into a single-objective solution for practical implementation. Benchmark tests using generational distance and spacing metric demonstrated the superior performance of the proposed algorithm over conventional multi-objective PSO and NSGA-II. Numerical simulations of the robotic arm further validate the method’s feasibility, yielding a well-converged Pareto front that effectively balances time-impact tradeoffs. The results demonstrated a computationally efficient framework for stability-critical trajectory planning in quadruped mobile manipulators.