The target problems of this study are global optimization problems with constraints in engineering fields. Differential Evolution(DE) is a well-known widely-used evolutionary algorithm among many metaheuristic algorithms and this study develops an algorithm based on it. Since DE has several algorithmic parameters to be set by a user, this study eliminates the need to tune those parameters a priori. This study proposes an algorithm based on DE, which includes automatic constraint handling technique and/or local approximate optimization to enhance the performance. Five engineering design problems are selected from literature and the proposed algorithms have been evaluated while comparing with a previous parameter-less method. The proposed methods show enhanced performance in convergence and the local approximate optimization clearly increases the performance of the algorithm for three out of five problems.