The high dimensionality of electronic nose data increases the difficulty of their use in classification models. Reducing this high dimensionality helps reduce variable numbers, improve classification accuracy, and reduce computation time and sensor cost. In this research, the Cyranose 320 electronic nose, which was used for apple defect detection, was optimized by selecting only the most relevant of its internal 32 sensors using different selection methods. The covariance matrix adaptation evolutionary strategy (CMAES), was applied and the average classification error rate of CMA over 30 random seed runs was 5.0% (s.d.=0.006). This study provided a robust and efficient optimization approach to reduce high data dimensionality of the electronic nose data, which substantially improved electronic nose performance in apple defect detection while potentially reducing the overall electronic nose cost for future specific applications.