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Restructuring and deregulation of the electricity sector have altered the behavior of market players, shifting the objective from cost minimization to profit maximization. Development of distributed generation units, along with concerns raised over the security of supply has prompted many customers to consider the installation of their own local capacity for generating electricity (and heat). This paper proposes a methodology for optimizing the operation of a portfolio of distributed units, based on profit maximization using genetic algorithms. Genetic algorithms are an optimization method based on the analogy with biological evolution, where the so-called population of solutions evolves through generations as a result of recombination, mutation and selection processes. Optimization is carried out based on the day-ahead forecast of hourly market prices of electricity. The method is tested on a set of distributed units, demonstrating the ability to find good solutions in an acceptable time period.