Cutting processes are arguably of utmost importance to the manufacturing of ordinary mechanical components. Nevertheless, they may significantly reduce the fatigue strength of metallic materials due to the damage arising from the complex stress state intrinsically generated by them. In particular, shear-cutting operations, such as punching and trimming, severely jeopardize the fatigue response of advanced high strength steels (AHSS), compromising the current trend of using this class of materials for weight reduction of automotive chassis components. Chassis parts are usually designed to withstand a high number of load cycles (over 106 cycles) throughout their lifetime. Thus, the availability of engineering data in the so-called high cycle fatigue (HCF) regime is crucial for the efficient design of such components. Commonly obtained by means of the standardized staircase method [1], the generation of experimental data characterizing the influence of cutting operations in the fatigue behavior of metallic grades is time-consuming, expensive and, hence, limited. For instance, a comprehensive characterization of the punching process should generate S-N curves considering different punch-die clearances for each material [2, 3]. Complementary to actual tests, numerical schemes which account for the manufacturing process in the fatigue simulations arise as important virtual-testing tools to bridge the gap of information left by the experimental data, assisting the engineers in the decision-making process of designing new components. In this work, a decoupled punching-fatigue scheme is proposed to numerically assess the HCF behavior of punched test specimens. Following a sequential approach, the punching process was first simulated through a plane strain finite element model. Then, the residual stress resulting from this simulation was set as initial stress in the fatigue analysis stage. The nonlinear HCF constitutive model initially proposed by Oller et al. [4] was adapted to properly incorporate the results of the manufacturing modeling into the fatigue simulations. The entire load history until the specimen failure is simulated. For the sake of computational efficiency, the cycle-jump algorithm presented in [5] was used as advance in time strategy. Preliminary numerical predictions show a good correlation with the number of cycles to failure obtained experimentally for different stress levels and materials.