The problem of mining high utility itemsets entails identifying a set of items that yield the highest utility values based on a given user utility threshold. In this paper, we utilize propositional satisfiability to model the Top-k high utility itemset problem as the computation of models of CNF formulas. To achieve our goal, we use a decomposition technique to improve our method’s scalability by deriving small and independent sub-problems to capture the Top-k high utility itemsets. Through empirical evaluations, we demonstrate that our approach is competitive to the state-of-the-art specialized algorithms.