Food waste is a problem for which solutions are recognised but not readily put into practice. What should be the primary objective, reducing or eliminating surplus food production, requires great change within social, cultural and economic structures. The secondary approach of redistributing surplus food to areas of deficit (in terms of socio-economic groups and/or geographic regions) involves a significant logistical burden, and suffers the same issues as with the elimination of waste. The least desirable, but perhaps most practicable approach, is the use of food waste as a feedstock for Anaerobic Digestion (AD). The strategic adoption of AD can therefore be seen as an important step towards mitigating food waste, but the implementation of efficient AD systems on a large (county/region) scale involves significant complexity. The optimal number, size and location of AD plants, and whether they are centralised versus decentralised, may be determined by considering factors such as supply and proximity to feedstock, transport links, emission hazards and social impact. Reaching balanced and objective decisions when faced with such disparate criteria is inevitably very difficult. To address this problem we prototype and evaluate a decision support tool for county-scale AD planning. Our approach is a hybridised Agent Based Model (ABM) with a Multi Objective Optimisation. We capture the spatio-temporal dependencies that exist in the water, energy and food systems associated with energy derived from food waste using Agent Based Modelling (ABM). The use of Interactive Multi Criteria Analysis as visual analytics offers a means to communicate the co-benefits and trade-offs that may emerge, as well as prioritise the AD strategies, based on the prioritization of criteria. Specifically, the method supports exploration of the social, environmental and economic impact of different AD strategies and decisions, linked to current issues, namely AD scale and adoption. The results highlight a trade-off between transport costs and social acceptability for the AD centralised versus decentralised strategies. When low carbon options are weighted higher then slow, steady and aggressive decentralised strategies are the best strategic adoption of AD. Conversely, when Energy production is considered a priority, then aggressive scaling up in a centralised approach is best with slow and steady approaches being further from the ideal. The framework has demonstrated that it permits a space for dialogue and transparent prioritization of AD strategies based on WEF nexus impacts.