<p>This paper proposes a trust region algorithm for constrained multiobjective optimization problems with both equality and inequality-type constraints. At every iterating point, a subproblem is formulated using the quadratic approximation of all the objective functions and linear approximation of all the constraints. The step is evaluated using the notion of actual reduction and predicted reduction. A non-differentiable penalty function is used to handle the constraint violations. An adaptive BFGS update rule is introduced to update the matrix at every iteration. A new formula to compute the trust region radius at every iteration is provided. In addition, a spreading technique is introduced to derive a well-spread Pareto front. The global convergence of the proposed algorithm is proved under some reasonable assumptions. Furthermore, the algorithm’s superlinear rate of convergence is established. Numerical results and comparisons with existing methods are provided using a set of test problems to show the efficiency of the proposed method.</p>