In this paper, we present a novel activity-based scheduling model that combines a continuous optimisation framework for temporal scheduling decisions (i.e. activity timings and durations) with traditional discrete choice models for non-temporal choice dimensions (i.e. activity participation, number and type of tours, and considered destinations). The central concept of our approach is that individuals resolve time conflicts that arise from overlapping activities, e.g. needing to work and desiring to shop at the same time, in order to maximise their derived utility. Our proposed framework has three primary advantages over existing activity scheduling approaches: (i) the time-conflicts between different temporal scheduling decisions are considered and resolved jointly; (ii) individual behavioural preferences are incorporated in the scheduling problem using a utility-maximisation approach; and (iii) the framework is computa- tionally scalable and can be used to estimate and simulate a city-scale case study in reasonable time. We introduce an estimation routine for the framework that allows model parameters to be calibrated using real-world historic data, as well as an efficient mixed-integer linear solver to optimally resolve temporal conflicts in simulated schedules. The estimation routine is applied and calibrated to a set of observed schedules in the Swiss mobility and transport mi- crocensus. We then use the optimisation program with the estimated parameters to simulate activity schedules for a synthetic population for the city of Lausanne, Switzerland. We validate the model results against reported schedules in the microcensus data. The results demonstrate the capabilities of our approach to simulate realistic, flexible schedules for a real-world case-study.