Cost-effectiveness of nonpoint-source pollution reduction programs in an agricultural watershed depends on the selection and placement of control measures within the watershed. Locations for best management practices (BMPs) are commonly identified through targeting strategies that define locations for BMP implementation based on specific criteria uniformly applied across the watershed. The goal of this research was to determine if cost-effectiveness of BMP scenarios could be improved through optimization rather than targeting. The optimization procedure uses a genetic algorithm (GA) to search for the combination of site-specific practices that meets pollution reduction requirements, and then continues searching for the BMP combination that minimizes cost. Population size, replacement level, crossover, and mutation parameters for the GA were varied to determine the most efficient combination of values. A baseline scenario, a targeting strategy, and three optimization plans were applied to a 1014 ha agricultural watershed in Virginia. All three optimization plans identified BMP placement scenarios having lower cost than the targeting strategy solution for equivalent sediment reduction. The targeting strategy reduced average annual sediment loss compared to the baseline at a cost of $42 per kg sediment reduction/ha. The optimization plan with the same BMP choices achieved the same sediment reduction at a cost of $36 per kg/ha. Allocation of BMPs varied among optimization solutions, a possibility not available to the targeting strategy. In particular the optimization solutions placed BMPs on several stream-edge fields that did not receive BMPs in the targeting strategy.