Several real-world optimization problems manipulate discrete variables, involve with many objectives and vary along the time, that is, they are dynamic. Recent works have focused on investigate dynamic multiobjective optimization problems (DMOPs), which adds an additional challenge to the search convergence. Some evolutionary strategies have emerged based on the adaptation of consagrated multiobjective algorithms previously proposed for static continuous optimization problems, such as, NSGA-II and MOEA/D. This work presents a novel evolutionary algorithm for dynamic discrete many-objective problems named D-MEANDS. It uses the subjacent search proposed in MEANDS. This algorithm has been successfully investigated in static MOPs and herein we propose some adaptations to be used in DMOPs. A comparative analysis of the new proposal is made using two DMOP evolutionary algorithms from the literature: DNSGA-II and MS-MOEA. A dynamic many-objective version of the knapsack problem (KP), known as dynamic multiobjective knapsack problem (DMKP), was explored. Results using DMKP formulations with 4 and 6 objectives suggest that D-MEANDS is a promising algorithm to deal with DMOPs with many objectives.