The improvement of diets from a nutritional and health perspective has been a critical policy objective in developing nations for the past few decades. However, the current stress that human populations are exerting on the planet has made it important to assess diets using environmental indicators, such as greenhouse gas (GHG) emissions. Therefore, the main objective of the current study was to propose a methodology in which Life Cycle Assessment results linked to dietary patterns in Peru were combined with nutritional and economic data to optimize diets. For this, a linear programming model was built in which the environmental, nutritional and economic information on a set of 25 dietary patterns in Peru were optimized in order to achieve the environmentally best-performing diet that complies with economic and nutritional standards. The result of the proposed linear program allowed understanding the amount of each individual food product that should be consumed in each city that satisfies all the restrictions included in the model in order to attain the lowest GHG emissions possible. Results demonstrated that GHG reductions in food diets can be attained through optimization. For instance, in the case of Lima the obtained reduction was 27%, lowering the annual per capita footprint linked to food diets to 690 kg CO2eq, as compared to the current value of 948 kg CO2eq. From an economic perspective, results show that there are important disparities between cities in terms of increasing or decreasing prices of the market basket. Considering that in most areas of the country food purchase accounts for approximately 50% of household expenditure, it is plausible to assume that food choice is a main carrier to achieve GHG emission mitigations. In this context, the method constitutes a useful tool for policy-makers to push forward joint regulations to improve health-related issues linked to the food diet and food choice together with recommendations to lower the climatic impact of diets.