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Planning as heuristic search has proven to be a powerful framework for domain-independent planning. Its effectiveness relies on the heuristic information provided by a state evaluator and the search algorithm used with this in order to solve the problem. This paper presents ordered hill climbing (OHC) search algorithm, which is used as a basis of a heuristic planner in conjunction with FF's relaxed planning graph heuristic. We present a novel way for extracting useful information to preorder neighborhoods of a search state before calling heuristic procedure to estimate them, by considering the high quality of the relaxed planning graph heuristic. In order to preserve completeness and improve search effort, a new restart strategy for complete search from local minimal is proposed when the local search guided by OHC fails. The ideas are implemented in our planner OHCP. Experiments in the STRIPS benchmark domains of international planning competitions (IPC) show that our algorithms can yield significant performance improvement as well as high quality solution plan.