Abstract: A rigorous mixed integer nonlinear programming (MINLP) model is developed to solve the integrated optimization problem of a heat exchange network integrated with the organic Rankine cycle. At the same time, a new enumeration algorithm is developed to decompose the complex MINLP model into mixed integer linear programming (MILP) and nonlinear programming (NLP) sub-models by adding the key constraints of stream matching and eliminating the repetitive network structure. Within this algorithmic framework, the first step involves iteratively solving the MILP model to enumerate all feasible network structures. Subsequently, for each network structure, a global solver (BARON) is employed to optimize the NLP model and determine the total annual cost (TAC) of that specific structure. Finally, through a comparison of the TACs of all network structures, the globally optimal design solution is determined. The case study demonstrates that, the proposed algorithm can converge to the global optimal solution in only 16 s, and the proposed network structure constraints can reduce the number of repetitive network structures by 81.25%, thus improving the optimization efficiency of the algorithm.