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Continuous, accurate, and quantitative non-destructive testing of bitter pit, as well as research on the phenotype of new varieties of apples by breeding experts, require the support of accurate bitter pit identification technology. The presence of bruise will interfere with the recognition of bitter pit and reduce the recognition accuracy. Therefore, it is necessary to carry out research on the recognition of bitter pit and bruise. Based on the CT images of apples, a method for identifying apple bitter pit and bruise was proposed. The method such as maximum between-class variance, region labeling and median filtering were used to segment 337 apple CT images and locate the injured area. Following this step, a total of 18 features of the shape, texture and location of the injured area were extracted. Additionally, the feature information was selected using two methods of multiple stepwise regression and class distance separability criterion. The common features selected by the two methods were used as the selected feature information. Finally, the support vector machine optimized by genetic algorithm and the support vector machine with default parameters were used to identify apple bitter pit and bruise. The recognition results showed that the overall recognition accuracy of the support vector machine optimized by the genetic algorithm was higher than 93%, and the overall recognition accuracy of the support vector machine algorithm with default parameters was higher than 84%. The recognition accuracy of the support vector machine optimized by the genetic algorithm was obviously better than that of the support vector machine with default parameters. The research results can be used to cultivate the phenotype analysis of apple bitter pit and bruise.