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The principle, the algorithm, and the applicable scope of the Monte Carlo method were analyzed. The non-uniform feature of the distribution of random points in workspace was analyzed, as well as the different signification between the points within the workspace and the points on the boundary. The error resulted from spatial workspace slicing was summarized. In order to optimize the boundary accuracy, based on the continuity of the mapping from joint space to workspace, by generating new random joint values in a sufficiently small neighborhood of the existed random joint values corresponding to the boundary points extracted before, new random points in workspace were generated, which distributed around the corresponding old boundary points. Then, from the newly generated points, more accurate boundary points could be extracted. The approach demonstrated to be effective to improve the boundary precision. To reduce the error caused by the slice thickness, in each slice interval, only a thin layer of points were used. A large number of tests illustrate that the algorithm works well.