University timetabling is an NP-hard problem, which means that the amount of computation required to find solutions increases exponentially with problem size. Timetabling is subject to hard constraints that must be satisfied in order to produce feasible timetables and soft constraints, which are not absolutely essential. This paper describes the Stochastic Optimisation Timetabling Tool (SOTT) that has been developed for university course timetabling. Genetic Algorithms (GA), Simulated Annealing (SA) and random search are embedded in the SOTT. The algorithms include a repair process, which ensures that all infeasible timetables are rectified. This prevents clashes and ensures that the rooms are sufficiently large to accommodate the classes. The algorithms also evaluate timetables in terms of soft constraints: minimising student movement; avoiding fragmentation in the timetables for students and lecturers; and satisfying lecturers’ preferences for the timing of classes. The algorithms were tested using two sets of timetabling data from a collaborating university. Both \{GA\} and \{SA\} produced very good timetables, but the results obtained from \{SA\} were slightly better than those using GA. However, the \{GA\} was 54% faster than SA.