Lightweight design is a key factor in general engineering design practice, however, it often conflicts with fatigue durability. This paper presents a way for improving the effectiveness of fatigue performance dominated optimization, demonstrated through a case study on suspension brackets for heavy-duty vehicles. This case study is based on random load data collected from fatigue durability tests in proving grounds, and fatigue failures of the heavy-duty vehicle suspension brackets were observed and recorded during the tests. Multi-objective fatigue optimization was introduced by employing multiaxial time-domain fatigue analysis under random loads combined with the non-dominated sorting genetic algorithm II with archives. While evaluating fatigue life within optimization loops, particularly for multiaxial random load fatigue in the time domain, is time-intensive, this study is to improve computational efficiency in two strategies: 1) the dynamic adjustment of target nodes from the finite element model, using a weighted sum prior to performing fatigue damage prediction, 2) considering the actual cracking positions observed during the durability test, weld seams, identified as high-risk areas, were incorporated into the fatigue life prediction and optimization process. The fatigue evaluation results were in alignment with durability test outcomes of the suspension brackets, and the final optimization results were explored in both design and objective fields. The Pareto front was then utilized to show the trade-off between the conflicting objectives of lightweight design and enhanced fatigue performance to meet the enhanced durability requirements. This underscores the methodology's practicality and reliability in improving the durability and lightweight performance of suspension components.