Fog computing has becoming an attractive solution to face the low responsiveness existing in cloud-based networks. With the rapid emerging of Internet of Things (IoT), more and more terminal nodes are offloading their tasks to nearby fog nodes, located at the network edge, in order to reduce the processing delay. However, this tasks offloading requires an efficient scheduling mechanism that considers both user preferences and fog-cloud requirements. Existing research works for task scheduling in fog-cloud computing networks have mainly focused on reducing task delay and the overall energy consumption, without considering user preferences regarding the fog nodes' constraints. In this work, we present a ranking based task scheduling method that aggregates both user preferences and fog nodes features using linguistic and fuzzy quantified proposition to rank fog nodes from the most to the least satisfactory one. Moreover, we used two parameters called least satisfactory proportion (lsp) and greatest satisfactory proportion (gsp) in order to distinguish the similarities. Experimental results show that our approach satisfies the user preferences, and provides a compromising solution between the average user satisfaction, execution delay and energy consumption.