There are many and varied areas of the real world domains that require optimal solutions to their problems. This domains in areas as healthcare, production, public transportation, and driver assistance. Its solutions affect the people involved as well as the organizational efficiency and cost of operations. Recently, research in automated planning optimization has gained importance due to the need for complete automation in such various applications. Hierarchical Task Network (HTN) Planning is hard task even in pure search version and looking for optimal solutions can only be hard. In fact, there are few optimal planners and there are a relatively small number of other planners which usually produce good solutions without guaranteeing the optimality. Therefore, the main objective of this paper is to improve the search technique of HTN planning by adapting Ant system algorithm into refinement planning process by means of plan selection strategy. The proposed search strategy AS-HTN fuses the Ant system to improve to overcome optimal plan challenges. The evaluation of our proposed framework showed an outstanding performance against many plan selection strategies.