In this paper, the classification rule-mining problem is considered as a multi-objective problem rather than a uni-objective one. Metrics like predictive accuracy and comprehensibility, used for evaluating a rule can be thought of as different criteria of this problem. Predictive accuracy measures the accuracy of the rules extracted from the dataset where as, comprehensibility is measured by the number of attributes involved in the rule and tries to quantify the understandability of the rule. Using these measures as the objectives of rule mining problem, this paper uses gene expression programming to extract some useful and understandable rule. The discovered rule/knowledge is expressed in the form of IF-THEN high-level statement. Gene expression programming recently been introduced as one of the components of evolutionary algorithms and its attributes like simple linear representation and easy to implement, motivate us to use for mining classification rule with multiple objectives. It is often criticized when applied to classification rule mining with multiple objectives, because of the amount of computational resources it requires. However, we believe that it has a lot of potential to perform global search by exploring a large space. The rule antecedent part may contain different combinations of predictor attributes while the consequent part contains only the goal attribute. The searching process is guided by a fitness function considering both predictive accuracy and comprehensibility. Experiments with several benchmark datasets have generated rules for each class with acceptable predictive accuracy and comprehensibility.