Manufacturers are continually faced with customer expectations to deliver products faster while assuring high reliability. Companies must diligently plan and execute accelerated life tests in order to insure future reliability performance is met. In many industrial applications, accelerated life test results involve considerations that hinder the ability of the analyst to easily define a suitable test plan. A methodology for defining efficient, yet discerning, tests could insure that corporate investments in reliability testing are properly selected to mitigate risk while minimizing cost. This dissertation consists of three main studies. First, classical design of experiment methods are extended to multi-stress accelerated life test plans. This investigation mitigates the uncertainty in model parameter estimation for non-linear models with censoring and constrained feasible design regions. An electronics industry case study serves as the motivation for this research. Inference comparisons are drawn against current best practices documented in the literature. Second, an alternate technique for determining an optimal stress test level is introduced using a generalized linear model framework. This approach achieves equivalent results to traditional maximum likelihood estimation techniques, but avoids the necessity to compute complex first and second partial derivatives. In addition, it avoids the convergence problems associated with locally optimal solutions. Finally, a sensitivity and robustness study demonstrates additional tools to use in the planning of accelerated life tests.