Inspired from the competition of sport teams in a sport league, an algorithm is presented for solving constrained optimization problems. A number of individuals (solutions) as sport teams compete in an artificial league for several weeks (iterations). Based on the league schedule in each week, teams play in pairs and their game outcome is determined in terms of win or loss, given known the playing strength (fitness value) along with the teams' intended formations. Modeling an artificial match analysis, each team devises a new formation/ playing strategy (a new solution) for the next week contest and this process is repeated for a number of seasons (stopping condition). Performance of the proposed algorithm is measured using test functions from a well-known benchmark commonly adopted to validate new constraint-handling techniques/ algorithms. Results obtained by the proposed approach are very competitive with respect to other comparator algorithms already developed for constrained optimization and testify that the new algorithm can be regarded as an efficient tool for optimization in the presence of constraints.