In general, as the size of the problem increases or the number of objectives to be optimized increases in multi-objective optimization problems, the distribution range of the Pareto optimal solution set in the search space expands. However, the expansion of the search space makes it difficult for the variable information of other solutions to contribute to generating new solutions. This study proposes a novel multi-objective quantum-inspired evolutionary algorithm based on isolation strategy (MQEA/I) that has the following characteristics. Each individual basically evolves in isolation using only its own personal best solution obtained in the past generation. Each individual can automatically shift from global search to local search. Furthermore, MQEA/I has only one parameter except for the population size and the termination condition used in many evolutionary algorithms. Our experimental results using multi-objective 0–1 knapsack problems show that MQEA/I obtained a more accurate non-dominated solution set than NSGA-II and SPEA2 in problems with many objectives and items.