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Semiconductor manufacturing and testing requires intensive capital investment. Practical experiences in this industry indicate that a few savings on capacity may result in millions of benefit per year. This paper focuses on the issues pertaining to (i) what type of and how many testers should be invested for forthcoming orders for wafer and chip of a semiconductor testing facility under budget constraint? and (ii) how to allocate tester capacity for the orders so that the profit of a company can be maximized? This research develops a genetic algorithm simultaneously to resolve the two issues. A mathematical model is constructed and its answer serves as a benchmark. The experimental results show that the proposed algorithm is robust to the change of budgets and algorithm parameters, and the performances are close to the ones provided by the mathematical model.