A multiobjective optimization strategy based dynamic weighting algorithm is proposed in this paper for the optimal brake capture accomplished by a mars explorer to achieve the targeted parking orbit with minimal fuel consumption. Comparing with the conventional single objective optimization strategy, a key advantage of the developed multiobjective optimization formulation is that it considers the fuel consumption as well as the targeting accuracy simultaneously to achieve the optimization likely. The dynamical model of mars explorer is established where the direction of finitethrust is contrary to its velocity. Then, focusing on the improvement of particle swarm optimization (PSO) performance, adaptive PSO technique with dynamically adjusting parameters is presented to address the particular challenges posed by the optimization design of complex dynamic system with highly nonlinear coupling. What’s more, the dynamic weighting strategy along with the adaptive PSO is applied on the multiobjective optimization between fuel consumption and targeting accuracy. The multiobjective optimization strategy demonstrates a higher probability of seeking less fuel consumption than comparable conventional single objective optimization strategy when the targeting accuracy is in conformity with the requirement. Thus it has remarkable application value in actual projects.