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Aiming to improve the convergence and diversity of multiobjective evolutionary algorithms (MOEAs) for solving complicated high dimensional multi-objective optimization problems, a multi-objective evolutionary algorithm based on orthogonal designing model (MOEA/D-OD) was proposed. Under the framework of multi-objective evolutionary algorithm with decomposition scheme as typical characteristics, the orthogonal designing model (ODM) was incorporated into decomposition mechanism. By utilizing ODM, the good genes carried by the recombinant parents were obtained by offspring to avoid blindness of searching to improve the convergence of the proposed algorithm. The decomposition mechanism was applied to selection to balance exploitation and exploration. MOEA/D-OD was compared with four stateoftheart MOEAs on 18 benchmark testing problems. Experimental results indicated that MOEA/D-OD can obtain good convergence while having uniform distribution and wild coverage for Pareto sets. The searching performance can stay well when solving complex problems with complicated PS. To validate its performance on constraint multiobjective optimization problems, the proposed MOEA/D-OD was applied to solve the I-beam with two conflict objectives. Compared with other algorithms, the uniformly distributed Pareto sets obtained by MOEA/D-OD showed its practicability for engineering problems, which was an effective approach for solving high dimensional and complicated multi-objective optimization problems. 