This thesis describes research on the use of a genetic algorithm (GA) to prescribe treatment plans for forest management at the stand level. Forest management refers to making decisions about when and where to intervene in the natural growth of forests to achieve objectives, such as enhancing the visual quality of a stand or maximizing timber yield. A prescription is a schedule of thinning treatments applied to stands over a planning horizon. When multiple management goals exist treatment prescription becomes a complex multi-objective problem. The effectiveness of a GA depends on selecting an appropriate representation and germane fitness function. These design decisions are reviewed, followed by a series of experiments testing the performance of the GA. Different parameter settings are compared and the GA is contrasted with some other heuristic search methods. The final experiment compares a plan created by the GA to a plan recommended by a human expert.