The study of formal nonmonotonic reasoning has been motivated to a large degree by the need to solve the frame problem and other problems related to representing actions. New efficient implementations of nonmonotonic reasoning, such as SMODELS and DLV, can be used to solve many computational problems that involve actions, including plan generation. SMODELS and its competitors are essential to implement a new approach to knowledge representation and reasoning: to compute solutions to a problem by computing the stable models (answer sets) of the theory that represents it. Marek and Truszczynski call this paradigm Stable model programming. We are trying to assess the viability of stable logic programming for agent specification and planning in realistic scenarios. To do so, we present an encoding of plan generation within the lines of Lifschitz’s Answer set planning and evaluate its performance in the simple scenario of Blocks world. Several optimization techniques stemming from mainstream as well as satisfiability planning are added to our planner, and their impact is discussed.