Surgery groups are clustered surgery procedure types that share comparable characteristics (e.g. expected duration). Scheduling OR blocks leaves many options for operational surgery scheduling and this increases the variation in usage of both the OR and downstream beds. Therefore, we schedule surgery groups to reduce the options for operational scheduling, ultimately bridging the gap between tactical and operational scheduling. We propose a single step mixed integer linear programming (MILP) approach that approximates the bed and OR usage and a simulated annealing approach. Both approaches are compared on a real-life data set and results show that the MILP performs best in terms of solution quality and computation time. Furthermore, the results show that our model may improve the OR utilization from 71% to 85% and decrease the bed usage variation from 53 beds to 11 beds compared to historical data. To show the potential and robustness of our model, we discuss several variants of the model requiring minor modifications. The use of surgery groups makes it easier to implementation our model in practice and, for operational planners, it is instantly clear where to schedule different types of surgery.