AI planning, or planning for short, is an important branch of AI and widely applied in many fields, e.g., job shop scheduling, transportation scheduling, robot motion planning, aerospace mission planning, etc. A plan (a sequence of actions) must achieve all goals eventually in traditional planning, where such goals are called hard goals. Nevertheless, in many practical problems, the key focus is not only on the realization of goals as soon as possible and the reduction of the cost of plans as low as possible, but also on other factors, e.g., resource consumption or time constraint. To this end, the concept of simple preference which is also called soft goals is introduced. In contrast to hard goals, a simple preference is allowed to be violated by a plan. In essence, simple preferences are used to measure the quality of plans, without affecting the existence of plans. Current research on simple preferences makes less progress and the quality of plans are often unsatisfactory. This study proposes an efficient approach for solving simple preferences which are modeled as a part of classical planning models. Moreover, SMT (satisfiability modulo theories) solver is employed to recognize the mutual exclusion relations among simple preferences for the purpose of preference reduction, relieving the burden of planers. The major advantages of this approach lie in: on one hand, the state space is largely reduced due to the pre-tailoring of simple preferences, and on the other hand, the existing fast planners can be utilized and there is no need to design specialized planning algorithm. The experimental results on benchmarks show that the proposed approach has sound performance in improving the quality of plans, especially suited for the situation where simple preferences are not independent of each other.