A data warehouse contains multiple views accessed by queries. One of the most important decisions in designing a data warehouse is selecting views to materialize for the purpose of efficiently supporting decision making. The search space for selecting materialized views is exponentially large, therefore, heuristics have been used to search a small fraction of the space to get a near optimal solution. In this paper, we explore the use of a genetic algorithm for materialized view selection based on multiple global processing plans for many queries. We apply genetic algorithm to the combination of three issues. The first issue is to choose the best global processing plan from multiple global processing plans. The second one is query optimization.The The last one is to select materialized views from a given global processing plan. The experiment shows that genetic algorithm is a feasible approach to solve our problem. Our experimental studies also indicate that the combination of genetic algorithm and heuristics delivers better solutions than purely genetic algorithm or purely heuristics used in terms of the minimized (query and maintenance) cost and the evaluation cost to obtain the minimized cost.