Classification rule mining, addressed a lot in machine learning and statistics communities, is an important task to extract knowledge from data. Most existing approaches do not particularly deal with data instances matched by more than one rule, which results in restricted performance. We present a two-phase multiobjective evolutionary algorithm which first aims at searching decent rules and then takes the rule interaction into account to produce the final rule sets. The algorithm incorporates the concept of Pareto dominance to deal with trade-off relations in both phases. Through computational experiments, the proposed algorithm shows competitive to the state-of-the-art. We also study the effect of a niching mechanism.