Modifiable Boltzmann selective pressure is investigated as a tool to control variability in optimizations using genetic algorithms. An implementation of variable selective pressure, modeled after the use of temperature as a parameter in simulated annealing approaches, is described. The convergence behavior of optimization runs is illustrated as a function of selective pressure; the method is compared to a genetic algorithm lacking this control feature and is shown to exhibit superior convergence properties on a small set of test problems. An analysis is presented that compares the selective pressure of this algorithm to a standard selection procedure.