This paper presents a new method to automatically decompose general Mixed Integer Programs (MIPs). To do so, we represent the constraint matrix for a general MIP problem as a hypergraph and relax constraints by removing hyperedges from the hypergraph. A Breadth First Search algorithm is used to identify the individual partitions that now exist and the resulting decomposed problem. We propose that good decompositions have both a small number of constraints relaxed and small subproblems in terms of the number of variables they contain. We use the multiobjective Nondominated Sorting Genetic Algorithm II (NSGA-II) to create decompositions which minimize both the size of the subproblems and the number of constraints relaxed. We show through our experiments the types of decompositions our approach generates and test empirically the effectiveness of these decompositions in producing bounds when used in a Lagrangian Relaxation framework. The results demonstrate that the bounds generated by our decompositions are significantly better than those attained by solving the Linear Programming relaxation, as well as the bounds found via random and greedy constraint relaxation and decomposition generation.