A real world engineering design problem usually has multiple conflicting objectives, which can easily lead to difficulty in optimizing these objectives at the same time. Multiobjective combinatorial optimization is not only an open theory problem, it also has important practical significance. After modeling the constrained multiobjective combinatorial optimization problem, a new optimization algorithm is presented in detail. The algorithm is different from existing multiobjective evolutionary algorithms in three aspects. The first is the two-layer encoding method. The second is that it hybridises the simulated annealing algorithm with the genetic algorithm to improve the global searching ability while maintaining parallel computing ability. The third is the decision making mechanism to evaluate candidate solutions with several design objectives. A numerical example study shows that the proposed algorithm is capable of dealing with multiobjective combinatorial optimization problems