Evolutionary algorithms are normally applied to mine association rules on quantitative data but most of them obtain enough similar rules due to that the usual behavior of these algorithms is to converge on the best solution of the problem. To overthrow this issue, in this paper we present NICGAR, a new Niching Genetic Algorithm to obtain a reduce set of different positive and negative quantitative association rules with a low runtime. To do that, we extract the rules based on the existence of a pool of external solutions that contains the best rule of each niche found in the search process according to several quality measures, we penalize similar rules by means of a process based on fitness sharing, and we restart the algorithm leading to a diverse population. Moreover, the user can tune the trade-off between the quality and diversity of the mined rules making use of two thresholds. Finally, a new measure has also been presented to assess the similarity between rules based on involved attributes and covered examples by the rules. The quality of our proposal is analyzed using statistical analysis and comparing with classical, mono-objective evolutionary, and multi-objective evolutionary approaches for mining association rules.