A novel metaheuristic optimization algorithm, named supply-demand-based optimization (SDO), is presented in this paper. SDO is a swarm-based optimizer motivated from the supply and demand mechanism in economics. This algorithm mimics both the demand relation of consumers and supply relation of producers. The proposed algorithm is compared with other state-of-the-art counterparts on 29 benchmark test functions and 6 engineering optimization problems. The results on the unconstrained test functions prove that SDO is able to provide very promising results in terms of exploration, exploitation, local optima avoidance and convergence rate. The results on the constrained engineering problems suggest that SDO is considerately competitive in terms of computational expense, convergence rate, and solution accuracy.