Rural development faces major challenges related to infrastructure, youth migration, employment shortages, and food security. Agriculture is a dynamic sector that is crucial in addressing these challenges. In this context, this work conducted a theoretical review of the optimization techniques used and the information available in a specific case study. From the above, two techniques were selected, linear programming and stochastic linear programming, to efficiently solve this problem. Four models were proposed to define the number of hectares to be planted for the crops \(i\) in the municipalities \(j\) , considering food security conditions and some limiting factors in each territory. The results indicate that the amount of food and income produced in the municipalities is significantly lower than that obtained with the proposed models; the tons of food produced represent 15% of the amount projected with the linear programming model and do not account for 1% of the income generated by said model. Likewise, it is observed that the third model, which has two stochastic parameters, yields better results regarding the number of hectares planted, the tons of food produced, and the income generated in comparison with the second model, which only defines one parameter. In conclusion, these optimization models allow analyzing municipalities’ participation and diversification in relation to food production in the region, and their results are better that those of current empirical decisions.