Cultural algorithms (CA) are inspired from the cultural evolutionary process in nature and use social intelligence to solve problems. Cultural algorithms are composed of a belief space which uses different knowledge sources, a population space and a protocol that enables exchange of knowledge between these sources. Knowledge created in the population space is accepted into the belief space while this collective knowledge from these sources is combined to influence the decisions of the individual agents in solving problems. Classification rules comes under descriptive knowledge discovery in data mining and are the most sought out by users since they represent highly comprehensible form of knowledge. The rules have certain properties which make them useful forms of actionable knowledge to users. The rules are evaluated using these properties represented as objective and subjective measures. Objective measures are problem oriented while subjective measures are more user oriented. Evolutionary systems allow the user to incorporate different rule metrics into the solution of a multi objective rule mining problem. However the algorithms found in the literature allow only certain attributes of the system to be controlled by the user. Research gap exists in providing a complete user controlled system to experiment with evolutionary multi objective classification rule mining. In the current study a Cultural Algorithm Toolkit for Classification Rule Mining (CAT-CRM) is proposed which allows the user to control three different set of parameters. CAT-CRM allows the user to control the evolutionary parameters, the rule parameters as well as agent parameters and hence can be used for experimenting with an evolutionary system, a rule mining system or an agent based social system. Results of experiments conducted to observe the effect of different crossover rates and mutation rates on classification accuracy on a bench mark data set is reported.