The Active Shape Models (ASM) is composed of two parts: the ASM shape model and the ASM search. The standard ASM, with the shape variance components all discarded and searching in image subspace and shape subspace independently, has blind searching and unstable search result. In this paper, we propose a novel idea, called Optimal Shape Subspace, for optimizing ASM search. It is constructed by both main shape and shape variance information. It allows the reconstructed shape to vary more than that reconstructed in the standard ASM shape space, hence is more expressive in representing shapes in real life. A cost function is developed, based on a careful study on the search process especially regarding relations between the ASM shape model and the ASM search. An Optimal Searching method using the feedback provided by the evaluation cost can significantly improve the performance of ASM alignment. This is demonstrated by experimental results.