The simulated annealing (SA) algorithm has proven to be a good technique for solving difficult combinatorial optimization problems. In engineering optimization the SA has emerged as an alternative tool to solve problems which are difficult to solve by conventional mathematical programming techniques. The algorithm's major disadvantage is that solving a complex system may be an extremely slow convergence process, using much more processor time than some conventional algorithms. Consequently, simulated annealing has not been widely accepted as an optimization algorithm for engineering problems. Attempts have been made to improve the performance of the algorithm either by reducing the annealing length or changing the generation and the acceptance mechanisms. However, these faster schemes, in general, do not inherit the SA properties of escaping from local minima. A more efficient way to reduce the processor time and make the SA a more attractive solution for engineering problems is to add parallelism. However, the implementation and efficiency of parallel SA models are in general problem dependent. Thus, this paper considers the evaluation of parallel schemes for engineering problems where the solution spaces may be very complex and highly constrained and function evaluations vary from medium to high cost. In addition, this paper provides guidelines for the selection of appropriate schemes for engineering problems. An engineering problem with relatively low fitness evaluation cost and strong time constraint was used to demonstrate the lower bounds of applicability of parallel schemes.