<p>Accelerated life testing is an old and useful method of testing the reliability of products, especially when it is not possible to test the product directly in the conditions that are realistic or over undue length of time. The need to have efficient and informative testing schemes has become quite more eminent with the advent of more durable materials. The paper constructs a statistical model of multiple of constant stress experiments, in such a way that it elicits as much inferential data as possible in situations where experimental resources are required to be limited. The approach combines the practical benefits of ordered ranked set sampling under the flexible form of progressive Type-II censoring, which is thought of here in the exponentiated exponential lifetime framework. Inferential part is carried out in the frequentist and Bayesian directions. Maximum likelihood estimation is used in the frequentist section, and the resulting intervals are obtained by using asymptotic approximations, and by bootstrap resampling which, in practice, has a stabilizing effect on the estimates. From the Bayesian view, both conventional and empirical Bayes estimators are reviewed under two loss functions—one symmetric, the other asymmetric—with corresponding credible bounds derived from the highest posterior density rule. Attention is next given to experimental design, where D-, A-, and C-optimality measures are invoked to indicate efficient configurations. Monte Carlo evaluations were, thereafter, carried out to follow the pattern of the estimators, and the outcomes suggest that Bayesian procedures, on the whole, yield smaller errors and somewhat greater precision, particularly in situations where sample data are meagre. Finally, a numerical example is presented to show how the method might be used within practical reliability assessment.</p>