Real-life optimization problems consist of various conflicting optimization problems. Due to the interdependency of these sub-optimization problems, their solution is more difficult than single optimization problems. For this reason, most of the real-life optimization problems are considered multi-objective optimization problems by nature, and different methods and approaches are applied. In this paper, a recent multi-objective optimization problem, the bi-objective travelling thief problem (BiTTP) was studied. Two state-of-the-art algorithms, ALNS and NSGA-II, were hybridized with certain local search methods and applied on six problem instances of Polyakovskiy’s problem benchmark set. As a result, the performances of these two algorithms were evaluated and a new set of Pareto front sets were generated for the related benchmark instances.