In order to reduce the computational complexity of multi-objective evolutionary optimization-based clustering algorithms, a Kriging-assisted reference vector guided multi-objective robust spatial fuzzy clustering algorithm (KRV-MRSFC) is proposed and then successfully applied to image segmentation. We first construct objective functions with noise robust local spatial information derived from the image to improve the robustness to noise and then use the Kriging model to approximate each objective function to decrease the computational cost. Meanwhile, in order to improve the approximation accuracy of the Kriging model, an angle-penalized distance-based expected improvement sampling criterion is presented in the KRV-MRSFC, which can select individuals with better exploitation and exploration to update the Kriging model. In addition, KRV-MRSFC adopts a clustering validity index with noise robust local image spatial information to select the optimal solution from the final non-dominated solution set to perform image segmentation. The experiments performed on Berkeley and real magnetic resonance images indicate that the proposed method not only achieves satisfactory segmentation performance on noisy images but also requires a low time cost.