Reasoning about preferences and uncertainty is very relevant in constraint optimization. Indeed, many real-life problems under constraints such as timetabling, scheduling, planning and logistics, involve a certain amount of preferences expressed in a qualitative way, i.e.," I prefer X more than Y" or in a quantitative way, i.e., "I prefer X with preference P" where P is a numerical value. In addition, many of these problems include uncertainty due to missing or imprecise information. We present a study of different methods combining quantitative and qualitative preferences in constraint problems. We also investigated the integration of incomplete knowledge in such problems. Finally, we propose a new model including preferences and uncertainty. The new model uses branch and bound technique for exploring the search space and it has been implemented in Java programming language.