Focusing on the issue that local search algorithm is prone to fall into the local optimum and does not adapt to the course arrangement under multiple constraints, an automated course arrangement algorithm based on multi-class iterated local search was proposed. Firstly, the course arrangement problems were classified by the multi-class classifier according to the characteristics of the problems to guide the neighborhood selection and parameter setting of the iteration local search. Then, in the process of iterated local search, the sequence-based greedy algorithm was used to obtain the feasible solutions. Finally, the problem characteristics oriented two-temperature control simulated annealing algorithm was used to search for local optimal solution in the neighborhood and the current optimal solution was perturbed by a specific strategy and iterated as the new initial solution to achieve global optimization. The proposed algorithm was tested on two internationally famous datasets, which are the second international timetabling competition dataset and Lewis 60 dataset. The experimental results show that, compared with the existing efficient algorithms in current literatures, the proposed algorithm has higher efficiency and solution quality.