Repeated measures degradation studies are used to assess product or component reliability when there are few or even no failures expected during a study. Such studies are used to assess the shelf life of materials and products. We show how to evaluate the properties of proposed test plans needed to identify statistically efficient tests. We consider test plans for applications where parameters related to the degradation distribution or the lifetime distribution are to be estimated. We use the approximate largesample variance-covariance matrix of the parameters of a mixed effects linear regression model for repeated measures degradation data to assess the effect of sample size (number of units and number of measurements within the units) on estimation precision of both degradation and failure-time distribution quantiles. We also illustrate the complementary use of simulation-based methods for evaluating and comparing test plans. These test-planning methods are illustrated with examples.