Multi-objective evolutionary algorithms are widely used in many engineering optimization problems and artificial intelligence applications. Ant lion optimizer is an outstanding evolutionary method, but two issues need to be solved to extend it to the multi-objective optimization field, one is how to update the Pareto archive, and the other is how to choose elite and ant lions from archive. We develop a novel multi-objective variant of ant lion optimizer in this paper. A new measure combining Pareto dominance relation and distance information of individuals is put forward and used to tackle the first issue. The concept of time weight is developed to handle the second problem. Besides, mutation operation is adopted on solutions in middle part of archive to further improve its performance. Eleven functions, other four algorithms and four indicators are taken to evaluate the new method. The results show that proposed algorithm has better performance and lower time complexity.