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The test selection and optimization (TSO) can improve the abilities of fault diagnosis, prognosis and health-state evalua-tion for prognostics and health management (PHM) systems. Tra-ditional y, TSO mainly focuses on fault detection and isolation, but they cannot provide an effective guide for the design for testa-bility (DFT) to improve the PHM performance level. To solve the problem, a model of TSO for PHM systems is proposed. Firstly, through integrating the characteristics of fault severity and propa-gation time, and analyzing the test timing and sensitivity, a testabi-lity model based on failure evolution mechanism model (FEMM) for PHM systems is built up. This model describes the fault evolution-test dependency using the fault-symptom parameter matrix and symptom parameter-test matrix. Secondly, a novel method of in-herent testability analysis for PHM systems is developed based on the above information. Having completed the analysis, a TSO model, whose objective is to maximize fault trackability and mini-mize the test cost, is proposed through inherent testability analysis results, and an adaptive simulated annealing genetic algorithm (ASAGA) is introduced to solve the TSO problem. Final y, a case of a centrifugal pump system is used to verify the feasibility and effectiveness of the proposed models and methods. The results show that the proposed technology is important for PHM systems to select and optimize the test set in order to improve their perfor-mance level.