The European Agri-Environmental Measures (AEMs) have a relevant role in encouraging a sustainable resources use and developing environmentally-friendly farming practices. AEMs account for more than half of the rural development budget of the Common Agriculture Policy. However, despite their importance, several factors influence the effectiveness of the measures, within which the poor spatial target is still a major cause of low effectiveness. Therefore, improving the spatial targeting of these policy tools could improve their cost-effectiveness, increasing the efficiency of Agri-Environmental Schemes (AES) and support better policy design solutions. The objective of this paper is to develop an optimization model for the AEMs jointly aiming at optimal targeting and payment setting with a focus on resource and incentive compatibility differentiated by zone. Moreover the model investigates the integration of information coming from spatial analysis of participation to AEMs with mathematical programming at regional level. This is a rather new methodology which could be uses to model farmers’ characteristics and compliance cost in their spatial dimension. Given that both the costs and the compensation payments are subject to spatial variation, this study simulates also the potential contribution of spatially differentiated compensation payments to efficient targeting of measure 214.1 in Emilia Romagna (Italy). Results highlight that the differentiate payment scheme gives a significant cost saving over flat rate mechanism by reducing farmers’ rents and consequently the deadweight loss for cost effectiveness of the measures. The method used, which improves the acknowledgement of the spatial information, may have a potential for the design process of Agri-Environmental Schemes (AES) and support better policy design solution.