As food safety shakes due to climate change and the looming possibility of a calamity similar to that of the COVID-19 pandemic, along with the increase in the world's population, there is an immediate need to increase food production by optimizing the agri-food supply chain. This work aims to help small-scale farmers manage this increase in orders and help them gear their resources towards more profitable practices by employing a multi-objective optimization model based on genetic algorithms that considers environmental aspects. To do so, we implement an asexual genetic algorithm that takes as input the demands received by the farmer and outputs the best combinations of demands to meet. The model takes into consideration the amount of land available for cultivation, as well as the resources (water, cost of cultivation, etc.) and revenue of the demands to determine the best combinations of demands to meet. This work is developed in the SMALLDERS framework and builds over the scarce literature that tackled the demand selection problem in the agricultural field.