Graph matching techniques are widely used in pattern recognition problems such as scene description, finger print identification, or face recognition. In this paper, we put forward two optimization methods for graph matching and compare them in the context of brain sulcus identification. The first approach is based on a constraint search in a neighborhood; the second uses a genetic algorithm for optimization. Experiments demonstrate that both methods yield satisfactory identification rates, however, the second method is more general and easier to adapt to similar problems.