The design of an experiment can greatly affect its potential to produce statistically conclusive results. In this paper, we offer a method for increasing the statistical informativeness of an experiment through the use of adaptive design optimization. The problem to be solved in adaptive design optimization is identifying an experimental design under which one can infer the underlying model and its parameter values in the fewest possible steps. While this problem is often impossible to solve analytically, it can be solved numerically with the help of a Bayesian computational trick that recasts it as a probability density simulation in which the optimal design is the mode of the density. The resulting optimization algorithm is flexible enough to apply in a variety of experimental settings. We demonstrate the effectiveness of this approach with a computer simulation of a memory retention experiment.