This chapter presents a study on the application of the principles of cooperative coevolution for design optimization in Concurrent Engineering (CE). The first section introduces the general and well-known CE practices that consider all elements involved in a product’s life cycle and emphasize executing all design tasks simultaneously. This results in various complex design problems in a typical CE setting involving many design parameters or different disciplinary knowledge to solve them. Methodologies for concurrent design that divide the original problem to simultaneously solve the smaller individual subproblems require good problem decomposition, optimization, and communication strategies among subproblems. The following section formulates two types of CE problems that consist of product design problems with different parts and quasi-separable Multidisciplinary Design Optimization (MDO) problems in which different disciplines share part of the design variables. Existing methods that have been developed to solve them are also briefly reviewed. The next section presents our proposal of a parallel framework of cooperative Coevolutionary Algorithm (CEA) for design optimization in CE that allows the development of two new concurrent design methods to solve two different types of CE problems. This is followed by a section that presents computational studies to investigate the efficacy of the new parallel cooperative CEA-based concurrent design methods on the universal electric motor design problems and a general multidisciplinary design optimization problem and compared to that of some existing methods. Furthermore, the impact of communication frequency among populations on the performances of the proposed methods is investigated in order to establish optimal communication frequencies under different communication costs and test problems. An outcome of the analysis is the development of an effective self-adaptive method that can be incorporated to the parallel cooperative CEA-based concurrent design methods to enable adaptation of the appropriate communication frequency during the optimization process. The chapter closes with a further discussion on the issue of communication in designing effective parallel cooperative CEAs for CE and further studies that can be done.