<p>This paper addresses uncertain multiobjective optimization problems by reformulating them into a deterministic form using objective-wise worst-case robust counterparts. To efficiently solve the resulting robust problem, we propose a BFGS-based quasi-Newton descent method combined with Wolfe-type line search strategies. The Wolfe-type inexact line search is employed to compute suitable step sizes, while a modified BFGS update guarantees the positive definiteness of the Hessian approximation at each iteration. We establish the global convergence of the proposed method and show that, under strong convexity assumptions, it achieves an R-linear convergence rate to a Pareto optimal solution. Moreover, for locally strongly convex objective functions with Lipschitz continuous Hessians, the algorithm exhibits Q-superlinear convergence. Numerical experiments compare the proposed method with the Armijo-type quasi-Newton method and the Newton method using performance profiles, demonstrating the efficiency and robustness of the proposed approach.</p>