In the evolutionary computation community, benchmark test functions have been used for evaluating performance of optimization algorithms. However, the conventional benchmark problems are not always based on practical problems in engineering fields. Therefore, in recent years, the importance of benchmark functions which reflect features of real-world problems has been pointed out. From the above background, Mazda Motor Corporation developed the simultaneous design optimization benchmark problem. This Mazda's benchmark problem has a key feature of the real-world design problems, i.e. many variables and many constraint conditions. In this research, we solve the Mazda's benchmark problem by using the ε constrained differential evolution (εDE), which is the combination of the ε constrained method and differential evolution (DE). In our proposed method, we consider the constrained optimization problem as a multiobjective optimization problem of minimizing the objective function and the constraint violation. To improve the search performance of εDE, we introduce control of the search direction based on the superior relationship of solutions on the objective function space. In the experiment, we compare the proposed method with the results of Evolutionary Computation Competition 2017. Experimental results show that the proposed method can achieve a proper balance between exploration and exploitation during search process and obtain good feasible solutions.