The problem of multibody motion segmentation is an important and challenging issue in computer vision. In this paper, a novel segmentation technique based on simulated annealing (SA) is proposed. According to the fact that under linear projection models, feature points of multibody reside in multiple subspaces, firstly, a meaningful energy function is proposed, which favors the correct formation of those subspaces, and some subspaces are generated as the initial state. Then, two strategies of subspace evolution and transformation are developed to optimize the energy function in a manner of simulated annealing. The ultimate configuration of these subspaces will reveal the inherent multiple subspace structure embedded in the data space. The classification of data points to these subspaces is equivalent to multibody grouping. The global optimization process results in an increase of robustness with noise tolerance. The method is also effective in degenerate cases. Promising results on synthetic and real data are presented.