Automated planning is one of the most important problems in artificial intelligence. We present a new refinement of the classical planning algorithm that formulates the planning problem as a satisfiability problem. Compared with previous techniques, the solution of the planning problem is identified using the number of truth assignments of the corresponding propositional formula and their actions' utilities. Our approach eliminates backtracking and supports efficient planners that consider additional subformulas without the need to recompute solutions for previously provided subformulas. The experimental results show that our approach can help existing SAT-based state-of-the-art planners to and the solution plan more efficiently.