This paper presents a novel teaching-learning-based optimization(TLBO) algorithm with group collaboration for the job shop scheduling problem(JSSP).Firstly, a collaborative learning strategy between groups is introduced to avoid being trapped into local optimum when solving the JSSP with high complexity.After learning within the group, individuals would communicate and collaborate with individuals of other groups so that they can jump out of the limitation of the current learning process.Then, in order to balance the local and global searching ability, a depth or width searching strategy is introduced, which enables individuals to learn in different ways according to their learning ability.For individuals with stronger learning ability, the depth learning will be adopted, and for individuals with weaker learning ability, the width learning will be adopted, so that different individuals can play different roles in the learning process.Experimental results on benchmark instances of OR-Library show that the search ability and convergence accuracy can been effectively improved in solving the JSSP.