In this study, we propose a novel feature selection technique to improve the classification of simultaneous anomalies in the context of predictive maintenance. We leverage three publicly available datasets collected from measurements and characterised by high dimensions. In this context, the use of machine learning algorithms with 5 multi-label classifiers has proven effective in anomaly classification tasks. Therefore, we implement a self-adaptive evolutionary strategy algorithm tasked with selecting the most relevant features. We assess the cost and the effectiveness of the classification task with and without feature selection. Overall, our results show that the self-adaptive evolutionary strategy is able to drastically reduce the number of features required for training, while improving or maintaining the performance of the classification task. Our study underscores the potential of feature selection for predictive maintenance multi-label classification tasks, contributing to more efficient and effective predictive maintenance strategies in industrial settings.