Inventory optimization is a significant problem that is tied directly to financial gains. Its complexity has led to the development of new inventory models and optimization techniques. Evolutionary algorithms, particularly Pareto based evolutionary algorithms have been proven to be reliable for solving such problems. However, these evolutionary algorithms concentrate mostly on global search and have limited local search abilities. This leads to a poor convergence to the Pareto front. Among these algorithms the most studied are non-dominated sorting genetic algorithm-II and strength Pareto evolutionary algorithm2. This paper proposes a novel method that increases their convergence. The novelty is based on three techniques: Firstly, a time-based fitness assignment that favours solutions from previous generations is employed. Secondly, before the crossover process, the mating pool is updated with a positive bias towards better solutions. Finally, a more disruptive mutation scheme is used to prevent premature convergence. The novel algorithms were tested on a benchmark problem suite and two inventory problems. The performance of the algorithms is measured using hypervolume, generational distance and spacing metrics. The results illustrated by graphics indicate that the novel algorithms can obtain better convergence without increasing the time complexity.