Differential Evolution (DE) is a novel evolutionary approach capable of handling non-differentiable, non-linear and multi-modal objective functions. DE has been consistently ranked as one of the best search algorithm for solving global optimization problems in several case studies. This paper presents an Improved Constraint Differential Evolution (ICDE) algorithm for solving constrained optimization problems. The proposed ICDE algorithm differs from unconstrained DE algorithm only in the place of initialization, selection of particles to the next generation and sorting the final results. Also we implemented the new idea to five versions of DE algorithm. The performance of ICDE algorithm is validated on four mechanical engineering problems. The experimental results show that the performance of ICDE algorithm in terms of final objective function value, number of function evaluations and convergence time.