A fundamental problem in multi-view 3D face modeling is the determination of the set of optimal views required for accurate 3D shape estimation for a generic face. There is no analytical solution to this problem, instead (partial) solutions require (near) exhaustive combinatorial search, hence the inherent computational difficulty. We build on our previous modeling framework which uses an efficient contour-based silhouette method and extend it by aggressive pruning of the view-sphere with view clustering and various imaging constraints. A multi-view optimization search is performed using both model-based (eigenheads) and data-driven (visual hull) methods, yielding comparable best views. These constitute the first reported set of optimal views for silhouette-based 3D face shape capture and provide useful empirical guidelines for the design of 3D face recognition systems.