Aiming at the problem that it is difficult to select multiple fault features of gearboxes in industrial environment, a new fault feature optimization selection method combining Fisher Score and maximum information coefficient(MIC) is proposed. First, considering about uneven distribution and overlapping of multi-fault features, the Fisher Score calculation method is used to construct the ranking rules of the importance of the feature indicators. Second, based on the impact of redundant features on the effective feature representation, the maximum information coefficient is used to update and rank redundant features. Then, taking classification accuracy as the judgement basis, using the support vector machine(SVM) theory, a fault feature optimization selection method combining Fisher Score and maximum information coefficient is established. Finally, the UCI standard data set and the gear failure simulation data set are used to verify the effectiveness and engineering practicability of the proposed algorithm. Comparative analysis of simulation experiments shows that compared with the traditional mRMR and reliefF methods, the number of feature subsets proposed is moderate and the accuracy is higher.