Many failure mechanisms can be traced to underlying degradation processes. Degradation eventually leads to a weakness that can cause a failure for products. When it is possible to measure degradation, such data often provide more information than traditional failure-time data for purposes of assessing and improving product reliability. For some products, however, degradation rates at use conditions are so low that appreciable degradation will not be observed in a test of practical time length. In such cases, it might be possible to use some accelerating variables (e.g., temperature, voltage, or pressure) to accelerate the degradation processes. In today’s manufacturing industries, accelerated destructive degradation tests (ADDTs) are widely used to obtain timely product reliability information. In designing an experiment, decisions must be made before data collection, and data collection is usually restricted by limited resources. Careful test planning is crucial for efficient use of limited resources: test time, test units, and test facilities. The basic goal in designing an experiment is to improve the statistical inference for the quantities of interest by selecting appropriate test conditions to minimize or control the variability of the estimator of interest. Generally, an ADDT plan specifies a set of testing conditions and the corresponding allocations of test units to each condition. In this dissertation, we study the test planning methods for designing accelerated destructive degradation tests from three aspects, including non-Bayesian and Bayesian methods. First, Chapter 2 presents the non-Bayesian methods for accelerated destructive degradation test planning when there is only one failure cause for the testing products. Second, Chapter 3 describes the non-Bayesian methods for accelerated destructive degradation test planning when more than one failure cause (sometimes known as competing risks) are induced for the produces which are tested at high-stress levels of accelerating variables. Third, Chapter 4 shows the Bayesian methods for accelerated destructive degradation test planning.