Accelerated life tests are commonly utilized in manufacturing industries to induce early failures of highly reliable
products. In this paper, estimation and optimal design issues of multiple ramp-stress accelerated life tests are discussed
for the generalized half-normal distribution. Assuming inverse power model as a life-stress relationship, the maximum
likelihood estimates of the parameters, as well as Fisher information matrix are derived. In addition, the methods of least
square, moments, Bayes are used for estimating the model parameters. The optimal proportion of test units allocated
to each stress level is obtained under D and A-optimality criteria. A sensitivity analysis of the optimal allocation to
misspecication of the model parameters is carried out. Furthermore, a real data set is used to show the application of
the generalized half-normal distribution in reliability studies. Finally, a Monte Carlo simulation study is carried out to
examine the performance of the estimation methods and the optimality criteria.