Computer software marketed by companies such as the Heat Transfer Research Institute (HTRI), HTFS, and B-JAC International are used extensively in the thermal design and rating of HEs. A primary objective in HE design is the estimation of the minimum heat transfer area required for a given duty, as it governs the overall cost of the HE. However, because the possible design configurations of heat transfer equipment are numerous, an exhaustive search procedure for the optimal design is computationally intensive. This paper presents a genetic algorithm (GA) framework for solving the combinatorial problem involved in the optimal design of HEs. The problem is posed as a large-scale, combinatorial, discrete optimization problem involving a black-box model, The problem is derived from earlier work on HE design using simulated annealing (SA). SA and GAs are particularly suitable in this black-box model because they lack the crucial gradient information required for other mathematical programming approaches. A methodology based on a command procedure has been modified to run the HTRI design program iteratively coupled to both SA and GAs. In our earlier studies, SA was found to be a robust and computationally efficient technique for the optimal design of HEs subject to infeasibilities and vibration problems. This paper compares the performance of SA and GAs in solving this problem and presents strategies to improve the performance of the optimization framework.