Multi-Objective Evolutionary Algorithms (MOEAs) are powerful tools for solving a wide range of real-world applications that involve the simultaneous optimization of several objective functions. However, their scalability to manyobjective problems remains as an important issue since, due to the large number of non-dominated solutions, the search is guided solely by the diversity criterion. In this paper, we propose a novel MOEA that incorporates a density estimator based on a visualization technique called Parallel Coordinates. Using this approach, a graph is represented by a digital image, where a pixel identifies the level of overlapping line segments and those individuals covering a wide area of the image have a high probability of survival. Experimental results indicate that our proposed approach, called Multiobjective Optimizer based on Value Path (MOVAP), outperforms existing algorithms based on clustering (SPEA2), crowding distance (NSGA-II), reference points (NSGA-III) and the hypervolume indicator (HypE) on most of the problems of the WFG test suite for five and seven objectives, while its performance in low dimensionality remains competitive.