Aimed at the problem that the multi objective particle swarm optimization algorithm in finding the solution easily gets into the local optimum by using the MOPSO algorithm to deal with the weapon target assignment, an improved multi objective quantum behaved particle swarm optimization (MOQPSO) algorithm is proposed. First, the improved MOQPSO algorithm is applied in solving the optimization model of firepower assignment with multilauncher and multiweapon by adjusting encode mode, modifying the position update formulas, introducing Gaussian mutation, and updating the external archives. Next, the improved MOQPSO and MOPSO algorithm are adopted to solve two battle suppositions with different scale. Finally, the convergence of the multiobjective optimization model is compared with that of the single objective optimization model. The simulation results indicate that the computation speed of the improved MOQPSO is about six times faster than that of the MOPSO, and the convergence of the Pareto solutions is high in precision and the diversity is even more, and the effectiveness and superiority of the improved MOQPSO algorithm are verified.