Metaheuristic-based data mining algorithms are frequently used in literature for discovering meaningful rules out of huge datasets. However, in the design criteria of these algorithms, the choice of objective functions affects the performance of the algorithm and classification accuracy. ABCMiner is one of these algorithms and is a classification rule learning algorithm based on a swarm based metaheuristic algorithm, Artificial Bee Colony algorithm. In this paper, the performances of two different objective functions on ABCMiner are evaluated. The experimental evaluation is conducted using real datasets.