Degradation models are usually used to provide information on the reliability of highly reliable products that are not likely to fail within a reasonable period of time under the traditional life tests or accelerated life tests. Gamma process is a natural model for describing degradation paths which exhibit a monotone increasing pattern, while the commonly used Wiener process is not appropriate in such a case. In this talk, we discuss the problem of optimal design for degradation tests based on a gamma degradation process with random effect. In order to conduct a degradation experiment efficiently, several decision variables (such as the sample size, inspection frequency, and measurement numbers) need to be determined carefully. These decision variables affect not only the experimental cost, but also the precision of the estimates of lifetime parameters of interest. Under the constraint that the total experimental cost does not exceed a pre-specified budget, the optimal decision variables are found by minimizing the asymptotic variance of the estimate of the 100p-th percentile of the lifetime distribution of the product. A laser data is used to illustrate the proposed method. Moreover, the effect of model mis-specification that occurs when the random effects are not taken into consideration in the gamma degradation model are assessed analytically. The numerical results of these effect reveal that the impact of model mis-specification on the accuracy and precision of the prediction of percentiles of the lifetimes of products are somewhat serious for the tail probabilities. A simulation study also shows that the simulated values are quite close to the asymptotic values.