Data Mining is one of the most important tools in discovering and analyzing knowledge from the data. Association Rule Mining is one technique of data mining. Many algorithms are used for mining association rules. The main disadvantage of using classical approaches for frequent pattern mining is that these are time consuming and the rules generated from these are not interesting and strong. The research aims to overcome the above shortcomings. To generate association rules, Apriori algorithm is combined with genetic algorithm. Genetic algorithm is applied on the frequent patterns which are obtained by the Apriori algorithm. Lift measure is used to measure the correlation in itemsets i.e. whether they are correlated negatively, positively or are independent of each other. The rules having a lift value greater than one are considered as having a positive dependence. Lift measure helps the users to discover their choice of rules. The use of lift parameter has reduced the number of rules generated and also reduced the time required.