At present,most of the research on test optimization selection is limited to the qualitative research on whether the fault can be diagnosed,without further considering the difficulty degree of fault diagnosis.To solve the above problem,a test optimization selection method based on quantitative evaluation of fault diagnosability is proposed.Firstly,the fault diagnosability is quantitatively evaluated based on Earth Movers Distance (EMD).Then,based on the evaluation results and comprehensively considering the number, reliability and cost of tests,a multi-objective test optimization selection problem is constructed,and a binary tuna swarm optimization algorithm is proposed to solve the optimal test set meeting the testability index and fault diagnosability level.Finally,a switching power supply of a certain type of equipment is taken as an experimental case to verify the effectiveness of the proposed method.The simulation results show that the proposed method can realize the test optimization selection aiming at improving the system fault diagnosability, and can fundamentally improve the system fault diagnosis ability.