The economic scheduling of Combined Heat and Power (CHP) systems is a very hard and complex problem. With a rise in the number of operational units and related constraints, the conventional mathematical techniques cannot be used for optimization and optimal scheduling of the CHP units. This problem aims to simultaneously minimize the overall cost of operation of the electrical generator, CHP and heat generator units with the consideration of various operational constraints. This problem is very challenging and difficult to solve since the objective function is nonconvex and has nonlinear characteristics. A popular optimization technique, the Particle Swarm Optimization (PSO) is used in this paper to solve CHPED problem. The algorithm mimics the social behaviour and the food searching strategy of birds or fishes, which usually move in groups in search of food. To demonstrate the algorithm's superiority in handling different related constraints and the ability to produce better results, it is implemented on three different test systems. The results obtained by PSO on all the test systems are compared with the results obtained using some of the latest published techniques, which highlights the superiority of the PSO algorithm.