As a key preprocessing step in classification, feature selection involves two conflicting objectives: maximizing the classification accuracy and minimizing the number of selected features. Therefore, multi-objective optimization is widely used in feature selection due to its excellent trade-off between the convergence of two objectives. However, most existing multi-objective feature selection methods still face the issues of the “curse of dimensionality” and high computational costs, especially when the search space is large. To solve the above issues, this paper proposes a bidirectional dynamic grouping multi-objective evolutionary approach for high-dimensional feature selection, referred to as BDGMOEA. This approach transforms a high-dimensional feature selection problem into a feature selection task with a smaller search space by the idea of feature grouping, in which one bit of an individual represents a group of features. Specifically, a grouping search strategy is developed to divide the features into different quadrants according to the importance of the features obtained by different evaluation techniques. Then, the features in each quadrant are grouped by sector. This strategy can effectively narrow the search space and quickly locate promising feature regions. In addition, a bidirectional dynamic adjustment mechanism is presented by considering the evolutionary state of the population, and it can be used to explore each feature in more detail and comprehensively to prevent good features from being ignored in unselected groups. The experimental results demonstrate that the proposed BDGMOEA method performs the best in most cases, indicating that BDGMOEA not only achieves better classification performance but also reduces the training time.