Proper tuning of control parameters is critical to the performance of a multi-objective evolutionary algorithm (MOEA). However, the developments of tuning methods for multiobjective optimization are insufficient compared to singleobjective optimization. To circumvent this issue, this paper proposes a novel framework that can self-adapt the parameter values from an objective-based perspective. Optimal parametric setups for each objective will be efficiently estimated by combining single-objective tuning methods with a grouping mechanism. Subsequently, the position information of individuals in objective space is utilized to achieve a more efficient adaptation among multiple objectives. The new framework is implemented into two classical DifferentialEvolution-based MOEAs to help to adapt the scaling factor F in an objective-wise manner. Three state-of-the-art single-objective tuning methods are applied respectively to validate the robustness of the proposed mechanisms. Experimental results demonstrate that the new framework is effective and robust in solving multi-objective optimization problems.