A novel method for the implementation of parallel genetic algorithms is introduced to locate the global minimum of a multidimensional function inside a rectangular hyperbox. The algorithm relies on a client - server model and incorporates an enhanced stopping rule. A number of experiments were conducted in order to measure the effects in termination by using the termination rule either on server machine or on clients. The method is tested on a series of well - known test functions as well as neural network training and the results was compared against another parallel genetic algorithm method. The results from the experiments are reported in terms of test error and amount of generations.