One of the main drawbacks in applying genetic algorithms (GAs) to complex problems has been the high computational cost due to their slow convergence rate. This fact gives a difficulty in an attempt to use GAs for function optimization problem. To alleviate this difficulty, an island-model based hybrid search method which combines a real-coded genetic algorithm(RCGA) with a simplex search method is presented. Our motivation for employing the RCGA is to introduce best exploration into the hybrid search method, and the simplex search method is to introduce cost-effective exploitation. In an attempt to make effective use of the exploitation operation of the simplex search method in the proposed search framework, we use a parallel architecture where two algorithms run during the isolation time and exchanges migrants. To demonstrate the superiority of the proposed algorithm, it is compared with an alternative optimization technique, RCGA proposed by Michalewicz in two optimization problems for modeling a system with time delay and tuning the parameters of a PID controller.