A model for simultaneous optimization of combinations of test-based decisions in psychology and education is proposed using Bayesian decision theory. The decision problem addressed consists of a combination of a placement and a mastery decision. Weak and strong decision rules are distinguished. As opposed to strong rules, weak rules are allowed to take prior test scores in the series of decisions into account. The introduction of weak rules makes the placement-mastery problem a multivariate decision problem. Conditions for optimal rules to take monotone forms are derived. Results from an empirical example of instructional decision making are presented to illustrate the differences between a simultaneous and a separate approach.