The goal of helping to automate the management of an individual's time is ambitious in terms both of knowledge engineering and of the quality of the plans produced by an AI system. Modeling an individual's activities is itself a challenge, due to the variety of activity, constraint, and preference types involved. Activities might be simple or interruptible; they might have fixed or variable durations, constraints over their temporal domains, and binary constraints between them. Activities might require the individual being at specific locations in order, whereas traveling time should be taken into account. Some activities might require exclusivity, whereas others can be overlapped with compatible concurrent activities. Finally, while scheduled activities generate utility for the individual, extra utility might result from the way activities are scheduled in time, individually and in conjunction. This article presents a rigorous, expressive model to represent an individual's activities, that is, activities whose scheduling is not contingent on any other person. Joint activities such as meetings are outside our remit; it is expected that these are arranged manually or through negotiation mechanisms and they are considered as fixed busy times in the individual's calendar. The model, formulated as a constraint optimization problem, is general enough to accommodate a variety of situations. We present a scheduler that operates on this rich model, based on the general squeaky wheel optimization framework and enhanced with domain-dependent heuristics and forward checking. Our empirical evaluation demonstrates both the efficiency and the effectiveness of the selected approach. Part of the work described has been implemented in the SelfPlanner system, a Web-based intelligent calendar application that utilizes Google Calendar.