While many frameworks for reasoning about time rely on the assumption that, as along as precedences are respected and there are no overlapping activities, any available time is just as good, all of us know that that is really not true! All of us would prefer avoiding heavy meetings right after lunch, waiting for connecting flights for endless hours or going on vacation at a selected destination during its rainy season. Among the many solutions to a temporal problem, being able select most preferred ones is a capability which is desirable in any intelligent system. On the other side, we may be often faced with over-constrained problems where relaxing hard constraints in a smart way can allow us to find a solution representing a better compromise. This contribution takes an AI perspective and shows how an efficient and expressive way to reason about preferences can help to handle time in a more flexible and sophisticated way. Among the many frameworks for temporal reasoning, constraint-based ones (quantitative and qualitative) have provided the most suitable base for the introduction of preferences. We will discuss how temporal constraints have been extended to allow for the representation of preferences, how temporal preferences can coexist with uncertainty and how they can be made conditional. Moreover, we will show how preferences allow for a significant increase in terms of representational power often at a modest additional computational cost.