<p>Life testing experiments are crucial for assessing product reliability, but traditional methods struggle with high product reliability, destructive testing, and time constraints. Stage Life Testing (SLT), which combines progressive censoring and step-stress testing, offers a flexible alternative by re-testing censored items under different stress levels. This article deals with the estimation methodology under SLT experiments using the tampered failure rate model, which assumes multiplicative effects on failure rates in different stress levels. The estimation procedure is proposed without making any specific assumptions about the shape of the hazard rate functions. Instead, it is assumed that the hazard functions in different stress levels belong to a class of distributions characterized by piecewise constant hazard rates. To address the challenges of complex maximum likelihood estimation, we propose an Expectation Conditional Maximization (ECM) algorithm, which efficiently handles parameter estimation. Simulation study is carried out to assess the performance of the proposed estimation procedure. Methodology for accommodating unknown cut-points is also proposed and illustrations using simulated data are presented. Extension to general <i>k</i>-step stage life testing is also considered. Finally, a computational algorithm is proposed to determine optimal censoring scheme based on D-optimality criteria.</p>