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In recent years, many algorithms for solving multiobjective (MO) optimization problems were proposed. Some studies on the design of multiobjective test problems and the performance evaluation of algorithms were also suggested. In this paper, we proposed a novel searching algorithm called the multiple trajectory search (MTS). The MTS uses multiple agents to search the solution space concurrently. Each agent does an iterated local search using one of four candidate local search methods. By choosing a local search method that best fits the landscape of a solution's neighborhood, an agent may find its way to a local optimum or the global optimum. We applied the MTS to the multiobjective optimization and tested it on the 13 benchmark problems provided for competition in the Special Session & Competition on Performance Assessment of Multi-Objective Optimization Algorithms in CEC2007.