High utility quantitative itemset mining refers to discovering sets of items that carry not only high utilities (e.g., high profits) but also quantitative attributes. Although this topic is very important to many applications, it has not been deeply explored and existing algorithms for mining high utility quantitative itemsets remain computationally expensive. To address this problem, we propose a novel algorithm named VHUQI (Vertical mining of High Utility Quantitative Itemsets) for efficiently mining high utility quantitative itemsets in databases. VHUQI adopts a vertical representation to maintain the utility information of itemsets in databases with several effective strategies integrated to prune the search space. The experimental results on both real and synthetic datasets show that VHUQI outperforms the state-of-the-art algorithms substantially in terms of both execution time and memory consumption.