Technology and innovations have long improved farming over the world and, as Industry 4.0 quickly spread, farmers have embraced high-level automation and data exchange, driving a transformation called Farming 4.0. Consequently, precise and even real-time field information have become easily accessible. Though, analyzing all this information requires great skills and tools, like mathematical knowledge and powerful computational algorithms to reach farmers expectations. This research explores the Crop Rotation Problem (CRP) and its relevance for the integration of Precision Agriculture (PA) and farm management. This paper presents a new mathematical approach for the CRP based on the nutrient balance and crop requirements, increasing the sustainable appealing of the problem. A real-encoded genetic algorithm (GA) was developed for optimization of the CRP. The results indicate good performance in mid and long-term crop scheduling.