This work examines the Capacitated Vehicle Routing Problem with Balanced Routes and Time Windows (CVRPBRTW). The problem aims at optimizing the total distance cost, the number of vehicles used, and the route balancing, under the existence of time windows and other constraints. The problem is formulated as a Multi-Objective Optimization Problem where all objectives are tackled simultaneously, so as to effect a better solution space coverage. A Multi-Objective Evolutionary Algorithm based on Decomposition (MOEA/D), hybridized with local search elements, is proposed. The application of local search heuristics is not uniform but depends on specific objective preferences and instance requirements of the decomposed subproblems. To test the efficacy of the proposed solutions, extensive experiments were conducted on well known benchmark problem instances and results were compared with other MOEAs.