The development of comprehensive grinding process models and computer-aided manufacturing provides a basis for realizing grinding parameter optimization. The variables affecting the economics of machining operations are numerous and include machine tool capacity, required workpiece geometry, cutting conditions such as speed, feed, and depth of cut, and many others. Approximate determination of the cutting conditions not only increases the production cost, but also diminishes the product quality. In this paper a new evolutionary computation technique, particle swarm optimization, is developed to optimize the grinding process parameters such as wheel speed, workpiece speed, depth of dressing, and lead of dressing, simultaneously subjected to a comprehensive set of process constraints, with an objective of minimizing the production cost and maximizing the production rate per workpiece, besides obtaining the finest possible surface finish. Optimal values of the machining conditions obtained by particle swarm optimization are compared with the results of genetic algorithm and quadratic programming techniques.