The primary difficulty of support vector machine (SVM) model selection is heavy computational cost, thus it is difficult for current model selection methods to be applied in face recognition. Model selection via uniform design can effectively alleviate the computational cost, but its drawback is that it adopts a single objective criterion which can not always guarantee the generalization capacity. The sensitivity and specificity as multi-objective criteria have been proved of better performance and can provide a means for obtaining more realistic models. This paper first proposes a multi-objective uniform design (MOUD) search method as a SVM model selection tool, and then applies this optimized SVM classifier to face recognition. Because of replacing single objective criterion with multi-objective criteria and adopting uniform design to seek experimental points that uniformly scatter on whole experimental domain, MOUD can reduce the computational cost and improve the classification ability simultaneously. The experiments are executed on UCI benchmark, and on Yale and CAS-PEAL-R1 face databases. The experimental results show that the proposed method outperforms other model search methods significantly, especially for face recognition.