Classification is an important topic in data mining field. Privacy preserving classification is a substantial subtopic that aims to perform classification of private data with satisfactory accuracy, while allowing sensitive information leakage at minimal level. Differential privacy is a strong privacy guarantee that determines privacy leakage ratio by using.. parameter; and enables privacy of individuals whose sensitive data are stored in a database. There exist some differentially private implementations of well-known classification algorithms such as ID3, random tree, random forests, Naive Bayes, SVM, logistic regression, k-NN etc. Although One Rule (1R) is a simple but powerful classification algorithm, any implementation of differentially private 1R classification algorithm has not been proposed in the literature to our best knowledge. Motivated by this gap, first we propose a differentially private 1R classification algorithm (DP1R), then improve its performance by using metaheuristics that are differential evolution (DE) and artificial bee colony (ABC) in this study. Additionally, we also apply DE and ABC to improve performance of differentially private Naive Bayes classifier and compare with DP1R. Moreover, DP1R is compared with the state-of-the-art differentially private algorithms such as differentially private SVM, differentially private logistic regression, differentially private ID3, and differentially private random tree on nine publicly available UCI datasets. The experimental results demonstrate that DP1R is an efficient classifier that has very similar accuracy to differentially private SVM which has the best accuracy results, however with respect to running time comparison of the methods, DP1R has the best performance among the all methods.