This paper describes an optimization procedure using Genetic Algorithm to define an
optimal accelerated test plan considering an economic approach. We introduce a general framework
to obtain plans of optimal accelerate tests with a specific objective, such as cost. The objective is to
minimize the costs involved in testing without reducing the quality of the data obtained. The
optimal test plans are defined by considering prior knowledge of reliability, including the reliability
function and its scale and shape parameters, and the appropriate model to characterize the
accelerated life. This information is used in Bayesian inference to optimize the test plan. To
perform optimization, a specific genetic algorithm is decribed and applied to obtain the best test
plan. This procedure is then illustrated on a numerical example
