In this paper, we study the planning and coordination of HRES and propose a model to characterize the determination of the optimal number of solar and wind power generators of HRES in uncertain environments. The goal is to achieve the minimum total cost, while satisfying the power demand of each area. To solve the model, a simulation optimization method, MSA, coupled with a Monte Carlo simulation approach, is proposed. In particular, the MSA method is based on the traditional simulated annealing method, but is further adapted by incorporating a metamodel-based search strategy to accelerate the algorithm convergence. Numerical results show that in comparison with the existing methods, the proposed MSA can have superior convergence performance under the same computational budget. Finally, a decision support system (DSS) integrating the proposed model and the solution methodology is developed as an efficient decision tool to enable effective and efficient energy management of HRES. The visualized outputs of DSS allow decision makers to gain better understanding about the management of HRES, facilitating the decision making process.