In this work, we focus on optimal energy management within the context of the tertiary control of a microgrid operating in grid-connected mode. Specifically, the optimal energy management problem is solved in a unified way by using the optimal power flow (OPF) and day-ahead concepts. The elements considered in the microgrid are a photovoltaic panel, a wind turbine, electric vehicles, a storage system, and a point of common coupling with the main grid. The aim of this paper consists of optimizing the economic energy dispatch within the microgrid considering known predictions of electricity demand, solar radiation, and wind speed for a given period of time. The OPF is solved using three different algorithms provided by the optimization toolbox of MATLAB® (R2015a, MathWorks®, Natick, MA, USA): the interior point method (IP), a hybrid genetic algorithm with interior point (GA-IP), and a hybrid direct search with interior point (patternsearch-IP). The efficiency and effectiveness of the algorithms to optimize the energy dispatch within the microgrid are verified and analyzed through a case study, where real climatological data of solar irradiance, wind speed in Almería city, photovoltaic system data, and room load from a bioclimatic building were considered.