In　this　paper,　an　adaptive　multi　-objective　differential　evolution　algorithm　based　on　evolutionary　process　information,　named　AMODE-EPI,　is　proposed　to　improve　the　searching　performance.　In　AMODE-EPI,　the　process　information　is　used　to　describe　the　schedule　of　evolution.　Meanwhile,　the　parameters,　including　scaling　factor,　crossover　rate　and　population　size,　are　adjusted　dynamically　based　on　EPI.　Then,　this　proposed　AMODE-EPI　can　balance　the　local　search　and　the　global　exploration　abilities.　Finally,　the　performance　of　AMODE-EPI　is　validated　and　compared　with　other　state-of-the-art　multi-objective　evolutionary　algorithms　on　a　number　of　benchmark　problems.　The　experimental　results　show　that　the　AMODE-EPI　has　better　convergence　and　diversity　than　the　other　algorithms.