This chapter discusses the application of evolutionary multi-objective optimization (EMO) to classification rule mining. In the field of classification rule mining, classifiers are designed through the following two phases: rule discovery and rule selection. In the rule discovery phase, a large number of classification rules are extracted from training data. This phase is based on two rule evaluation criteria: support and confidence. An association rule mining technique such as Apriori is usually used to extract classification rules satisfying pre-specified threshold values of the minimum support and confidence. In some studies, EMO algorithms were used to search for Pareto-optimal rules with respect to support and confidence. On the other hand, a small number of rules are selected from the extracted rules to design an accurate and compact classifier in the rule selection phase. A heuristic rule sorting criterion is usually used for rule selection. In some studies, EMO algorithms were used for multi-objective rule selection to maximize the accuracy of rule sets and minimize their complexity. In this chapter, first we explain the above-mentioned two phases in classification rule mining. Next we explain the search for Pareto-optimal rules and the search for Pareto-optimal rule sets. Then we explain evolutionary multi-objective rule selection as a post processing procedure in the second phase of classification rule mining. A number of Pareto-optimal rule sets are found from a large number of candidate rules, which are extracted from training data in the first phase. Finally we show experimental results on some data sets from the UCI machine learning repository. Through computational experiments, we demonstrate that evolutionary rule selection can drastically decrease the number of extracted rules without severely degrading their classification accuracy.We also examine the relation between Paretooptimal rules and Pareto-optimal rule sets.