In optimizing queries, solutions based on AND/OR DAG can generate all possible join orderings and select placements before searching for optimal query execution strategy. But as the number of joins and selection conditions increase, the space and time complexity to generate optimal query plan increases exponentially. In this paper, we use join graph for a relational database schema to either pre-compute all possible join orderings that can be executed and store it as a join DAG or, extract joins in the queries to incrementally build a history join DAG as and when the queries are executed. The select conditions in the queries are appropriately placed in the retrieved join DAG (or, history join DAG) to generate optimal query execution strategy. We experimentally evaluate our query optimization technique on TPC-D/H query sets to show their effectiveness over AND/OR DAG query optimization strategy. Finally, we illustrate how our technique can be used for efficient multiple query optimization and selection of materialized views in data warehousing environments.