Constrained optimization problems have been handled in the field of applied mathematics. On the other hand, evolutionary computations, a kind of computationally intensive methods are now applied to many applications. This paper presents RasID-GA (an abbreviation of Adaptive Random Search with Intensification and Diversification combined with Genetic Algorithm) for constrained optimization problems. The conventional constrained optimization methods use penalty functions to solve given problems. But, it is generally said that the penalty function is have to handle. In the proposed method, parallel RasIDs are combined with GA, and can find the optimal solution of constrained problems efficiently and effectively without using penalty functions.