Clustering algorithms are crucial in data mining and pattern recognition. However, most existing clustering methods operate based on a single granularity, which requires frequent distance calculations between sample points. These methods often exhibit unstable performance when handling high-dimensional data and clusters with complex shapes. To address this issue, this paper proposes a multi-objective driven granular ball clustering (MOGBC) algorithm, where granular balls represent a relatively coarse granularity with respect to the data. In the granule ball generation phase, principal component analysis (PCA) is used to determine the splitting direction of the granular ball. In the optimization phase, the multi-objective alignment loss function is minimized by balancing intra-cluster compactness (ICC) and inter-cluster separation (ICS). A neighborhood voting mechanism is also employed to determine the assignment of fuzzy boundary points. Experimental results demonstrate that MOGBC achieves strong clustering performance across 10 datasets, with significant advantages in clustering evaluation metrics such as ACC, NMI, and ARI, thereby validating the algorithm’s effectiveness and robustness.