This paper proposes a multiobjective quantum-inspired evolutionary algorithm (MQEA) to design efficient fuzzy path planner of mobil robot. MQEA employs the probabilistic mechanism inspired by the concept and principles of quantum computing. As the probabilistic individuals are updated by referring to nondominated solutions in the archive, population converges to Pareto-optimal solution set. In order to evaluate the performance of proposed MQEA, robot soccer system is utilized as a mobile robot system. Three objectives such as elapsed time, heading direction and posture angle errors are designed to obtain robust fuzzy path planner in the robot soccer system. Simulation results show the effectiveness of the proposed MQEA from the viewpoint of the proximity to the Pareto-optimal set. Moreover, various trajectories by the obtained solutions from the proposed MQEA are shown to verify the performance and to see its applicability.