This dissertation research emphasizes explicit Building Block (BB) based MOEA performance and detailed symbolic representations. An explicit BB-based MOEA for solving constrained and real-world MOPs is developed, the Multiobjective Messy Genetic Algorithm II (MOMGA-II) to validate symbolic BB concepts. The MOMGA-II provides insight into solving difficult MOPs that is generally not realized through the use of implicit BB-based MOEA approaches. This insight is necessary to increase the effectiveness of all MOEA approaches. Parallel MOEA (pMOEA) concepts are presented to potentially increase MOEA computational efficiency and effectiveness. Communications in a pMOEA implementation is extremely important, hence innovative migration and replacement schemes are detailed and tested. These parallel concepts support the development of the first explicit BB-based pMOEA, the pMOMGA-II. MOEA theory is also advanced through the derivation of the first MOEA population sizing theory. The sizing theory presented derives a conservative estimate of the MOEA population size necessary to achieve good results with a specified level of confidence. Validated results illustrate insight into building block phenomena, good efficiency, excellent effectiveness, and motivation for future research in the area of explicit BB-based MOEAs.