While contemporary layout optimization considers losses in energy production due to wake effects, the effects of blade fatigue remain mostly underrepresented. However, evaluating this coupled mechanism within the design loop is critical to accurately assess the levelized cost of energy (LCOE) under more realistic assumptions. In this paper, a layout optimization method that evaluates energy production costs based on turbine lifetime expectancy and accounts for wake-induced blade fatigue is presented. The method is used to study the effects on optimal layouts and park economics. Turbine lifetime expectancy is estimated using Miner's cumulative rule, including wake-induced turbulence, asymmetry introduced by wake steering, and azimuth-dependent variations in blade loading, to determine individual remaining lifetimes. This work compares three optimization scenarios that differ in whether the objective accounts for lifetime degradation and quantifies the resulting differences in the levelized cost of energy and optimal turbine placement. We find that the annual-energy-production (AEP) landscape of the studied site contains multiple near-degenerate optima, while blade fatigue varies steeply across these layouts, so a lifetime-aware objective helps in selecting between layouts that a pure AEP objective cannot distinguish. Under a demonstration reference damage equivalent load (DEL), anchored such that the most wake-exposed turbine of the AEP-optimal layout retains half of its assumed design life, the AEP-optimal layout carries a hidden lifetime-aware LCOE penalty of 7% that is invisible to a standard fixed-lifetime objective, and a layout optimized directly against the lifetime-aware LCOE removes this penalty at no cost in energy yield. The fatigue model is calibrated against single-turbine OpenFAST and two-turbine FAST.Farm wake simulations. Applying a power-maximizing wake-steering pass to each optimized layout further increases AEP and, despite the added once-per-revolution fatigue loading of the yawed rotors, moderately extends the remaining life of the most-exposed turbine. The results show that for the site assumed in this work, lifetime-aware LCOE optimization identifies the best available layout and lowers the worst-case fatigue loading.