Association rule mining has been studied from various perspectives, all of which have made valuable contributions to data science. However, there are promising research lines, such as the inclusion of continuous variables and the combination of numerical and categorical attributes for a supervised classification variety. This research presents a new alternative for solving the numerical association rule-mining problem from an optimization perspective by using the VMO (Variable Mesh Optimization) meta-heuristic. This work includes the ability for classification when categorical data are available from a defined rule schema. Our technique implements an optimization process for the intervals of continuous variables, unlike others that discretize these types of variables. Some experiments were carried out with a real dataset to evaluate the quality of the rules obtained; in addition to this, this technique was compared with four population-based algorithms. The results show that this implementation is competitive in classification cases and has more satisfactory results for completely numerical data.