No existing multi-objective evolutionary algorithms (MOEAs) have ever been applied to problems with more than 1000 real-valued decision variables. Yet. the real world is full of large and complex multi-objective problems. Motivated by the recent success of SaNSDE , an adaptive differential evolution algorithm that is capable of dealing with more than 1.000 real-valued decision variables effectively and efficiently, this paper extends the ideas behind SaNSDE to develop a novel MOEA named MOSaNSDE. Our preliminary experimental studies have shown that MOSaNSDE outperforms state-of-the-art MOEAs significantly on most problems we have tested, in terms of both convergence and diversity metrics. Such encouraging results call for a, more in-depth study of MOSaNSDE in the future, especially about its scalability.