Numerous multistage planning problems in finance involve nonlinear and nonconvex decision controls. One of the simplest is the fixed-mix investment strategy. At each stage during the planning horizon, an investor rebalances her/his portfolio in order to achieve a target mix of asset proportions. The decision variables represent the target percentages for the asset categories. We show that a combination of Tabu Search and Variable Scaling generates global optimal solutions for real world test cases, despite the presence of nonconvexities. Computational results demonstrate that the approach can be applied in a practical fashion to investment problems with over 20 stages (20 years), 100 scenarios, and 8 asset categories. The method readily extends to more complex investment strategies with varying forms of nonconvexities.