Optimized association rules are permitted to contain uninstantiated attributes.The optimization procedure is to determine the instantiations such that some measures of the roles are maximized.This paper tries to maximize interest to find more interesting rules.On the other hand,the approach permits the optimized association rule to contain uninstantiated numeric attributes in both the antecedence and the consequence.A naive algorithm of finding such optimized rules can be got by a straightforward extension of the algorithm for only one numeric attribute.Unfortunately,that results in a poor performance.A heuristic algorithm that finds the approximate optimal rules is proposed to improve the performance.The experiments with the synthetic data sets show the advantages of interest over confidence on finding interesting rules with two attributes.The experiments with real data set show the approximate linear scalability and good accuracy of the algorithm.