Multi-objective　differential　evolution　(MODE)　algorithm　is　an　effective　method　to　solve　multi-objective　optimization　problems.　However,　in　the　absence　of　any　information　of　evolution　progress,　the　optimization　strategy　of　MODE　algorithm　still　appear　as　an　open　problem.　In　this　paper,　a　dynamic　multi-objective　differential　evolution　algorithm,　based　on　the　information　of　evolution　progress　(DMODE-IEP),　is　developed　to　improve　the　optimization　performance.　The　main　contributions　of　DMODE-IEP　are　as　follows.　First,　the　information　of　evolution　progress,　using　the　fitness　values,　is　proposed　to　describe　the　evolution　progress　of　MODE.　Second,　the　dynamic　adjustment　mechanisms　of　evolution　parameter　values,　mutation　strategies　and　selection　parameter　value　based　on　the　information　of　evolution　progress,　are　designed　to　balance　the　global　exploration　ability　and　the　local　exploitation　ability.　Third,　the　convergence　of　DMODE-IEP　is　proved　using　the　probability　theory.　Finally,　the　testing　results　on　the　standard　multi-objective　optimization　problem　and　the　wastewater　treatment　process　verify　that　the　optimization　effect　of　DMODE-IEP　algorithm　is　superior　to　the　other　compared　state-of-the-art　multi-objective　optimization　algorithms,　including　the　quality　of　the　solutions,　and　the　optimization　speed　of　the　algorithm.