Accelerated life tests (ALTs) are widely used in industry to assist in product development. Acceleration models are often obtained from physical principles or past experience. But in some applications, particularly for investigating a new material or a new product, their acceleration models cannot be precisely specified. The uncertainty in model specification may cause serious problems in failure time prediction, and reduce the statistical efficiency of an optimal test plan. In this paper, the D and Ds optimal criteria are proposed for designing ALT plans that are good at selecting the best acceleration model among rival models. A generalized linear model (GLM) is developed for modeling ALT data with censoring. This approach simplifies the derivation of the information matrix of a test plan, and allows the experimenter to develop optimal ALT plans under the GLM framework. The proposed optimal design approach is compared with other conventional approaches through examples. The high design efficiency and design flexibility of the proposed approach are demonstrated in the paper.