In constraint programming the search strategy entirely guides the solving process, and drastically affects the running time for solving particular problem instances. Many features have been defined so far for the design of efficient and robust search strategies, such as variables' domains, constraint graph, or even the constraints triggering fails. In this paper, we propose to use the objective functions of constraint optimization problems as a feature to guide search strategies. We define an objective-based function, to monitor the objective bounds modifications and to extract information. This function is the main feature to design a new variable selection heuristic, whose results validate human intuitions about the objective modifications. Finally, we introduce a simple but efficient combination of features, to incorporate the objective in the state-of-the-art search strategies. We illustrate this new method by testing it on several classic optimization problems, showing that the new feature often yields to a better running time and finds better solutions in the given time.