Shape optimization based electronic prototyping is part of any future scenario for intelligent manufacturing. Unfortunately, the resulting optimization problems can be prohibitively large. Moreover, it is known that most of the optimization problems belong to the class of "hard" problems. Thus, seeking parallel methods for their solution is well justified. The idea of divide and conquer is already used to approximate the solution of large optimization problems sequentially. This suggests that it might be more efficient to decouple the system into several smaller subsystems, optimize them locally and in parallel, and then approximate the global solution by coordinating some form of global optimization on the interfaces of the subsystems. This approach is referred to as a two-level scheme. In general, an optimization problem involving many variables and constraints cannot be decomposed into independent subproblems that can be independently optimized. However, the above described problem decomposition approach yields good approximations to the global minimum while allowing the parallel application of shape optimization on local subsystems. The effectiveness of the two-level scheme comes from the inherent parallelism in modeling physical objects. The analysis and shape optimization are implemented using the parallel mesh and mesh splitting tool and its algorithmic infrastructure. For the shape optimization problem, we are developing two-level semi-optimal algorithms based on local and global mesh and decomposition data.