This study responds to the increasing market demand for manufacturers to provide reliable information about the longevity of their products. Manufacturers are particularly interested in the 100p-th percentile of a product’s lifetime distribution. Degradation tests are vital for this, as they offer insights into the product’s lifespan under various conditions over time. We propose a novel optimization method for designing a bivariate degradation test based on gamma processes, where the variables are dependent. We test various copula functions and select the best copula using the Akaike Information Criterion. This method optimizes the number of samples to be tested, the frequency of measurements, and the number of measurements, all while considering the constraints of experimental costs. This strategic approach ensures effective and efficient reliability prediction, catering to both the technical needs of engineering systems and the market demands for product reliability information. Finally, we test our model on a numerical example.