This work proposes the use of evolutionary algorithms to build individual knowledge rules with specific properties that are usually neglected when conducted by traditional supervised learning methods. The proposed evolutionary algorithm uses a rank-based, multi-objective fitness function that enables the arrangement of any set of measures. Experimental results that show the suitability of our proposal are also presented.