This paper presents an efficient strategy based on simulated annealing (SA), an algorithmic procedure for large-scale combinatorial optimization problems, for the optimal design of heat exchangers. The general heat exchanger design problem can be posed asa large-scale discrete optimization problem, and SA was found to be well suited for this type of heat exchanger design problem. A methodology based on a command procedure has been developed to run the HTRI design program coupled to the annealing algorithm, iteratively. At first, initial runs were made using the command procedure developed to determine the key annealing parameters. These parameters were then used to study several test cases pertaining to the general heat exchanger design problem involving infeasible configurations and vibration constraints. The analyses were performed using two different objective functions namely, total heat transfer area and a linearized purchased cost index. Lastly,the variable set governing the different configurations was extended to incorporate a larger set of design variables. It was observed that, in almost all cases, the optimum designs obtained using the simulated annealing algorithm yielded better performance dr cost functions compared to the base case (Amoco) designs. It has also been shown that an improvement in heat exchanger designs is achievable by extending the variable set to include a larger set of design alternatives. Simulated annealing offers great computational savings (in terms of CPU time) as a search strategy and has been found to be st robust technique for the optimal design of heat exchangers subject to process infeasibilities and vibration constraints.