Pre- and post-harvest stages are rarely considered together in modeling studies of fruit quality and disease development, despite evident connections between these two fruit life stages in terms of underlying processes. In order to fill this gap, a new modeling framework has been introduced to simulate the effects of pre-harvest practices (irrigation regime and fruit thinning intensity) and storage conditions (temperature and relative humidity) on the development of fruit quality traits during the growing season and storage, fruit yield, and the appearance of brown rot infections during storage. The model was specifically built and calibrated for nectarine (Prunus persica var. nucipersica). A set of performance criteria was defined based on model outputs describing fruit quality (fruit size and sweetness), yield and fruit loss during storage (caused by excessive mass loss or brown rot infections). A sensitivity analysis was then carried out to study the relationships between performance criteria and pre-harvest practices and storage conditions. Finally, the model was used in combination with an optimization algorithm to retrieve the pre- and post-harvest scenarios that maximized fruit quality and quantity, aggregated into a unique performance score. This was done for different storage times and the relative importance assigned to fruit quality criteria. The results revealed that irrigation regime and thinning intensity were significant in defining fruit quality traits, while storage conditions influenced fruit loss during storage. The interactions between pre- and post-harvest conditions were also found to be important when considering the fruit loss related to brown rot infections and the fruit yield at the end of storage. The importance assigned to quality criteria largely affected the optimization outcomes. Thus, the best scenario had moderate water deficit and a low-to-medium thinning intensity when the importance of the fruit sweetness index was high, and well-irrigated regimes and very low thinning intensity when it was lower. The results also indicated a trade-off between quality criteria and, in particular, sweetness and fruit yield. The proposed modeling framework highlighted the fact that the pre- and post-harvest conditions should be considered together because they can influence both fruit quality and quantity, including fruit loss due to brown rot. The model could therefore be used as a tool to enhance dialogue between fruit supply chain actors and to identify solutions to meet their expectations.