This work presents a dispatch optimizer algorithm that seeks to maximize the revenues of a CSP plant by selling electricity to the grid for a case study in Chile. The model comprehends a mixed-integer programming problem in which the operation and performance of the plant are represented by the physics and energy balances of the system. The analysis was applied using meteorological and electrical prices data of Crucero and Polpaico located in northern and central region of Chile, respectively, to couple a perfect forecast of weather and market conditions to the model. Results indicate that it is possible to determine best configurations for CSP plants in terms of solar field size and thermal storage under given electric prices conditions that result in better trade-offs between investment and profits of the plant. In this way, this work also demonstrates the importance of coupling optimized dispatch strategies with financial models, suggesting also that CSP developers could implement these type of optimizers with price forecasting models to evaluate the profitability and viability of their business models.