Life-history theory postulates that evolution is constrained by trade-offs (i.e., negative genetic correlations) among traits that contribute to fitness. However, in organisms with complex life cycles, trade-offs may drastically differ between phases, putatively leading to different evolutionary trajectories. Here, we tested this possibility by examining changes in life-history traits in an aphid species that alternates asexual and sexual reproduction in its life cycle. The quantitative genetics of reproductive and dispersal traits was studied in 23 lineages (genotypes) of the bird cherry-oat aphid Rhopalosiphum padi, during both the sexual and asexual phases, which were induced experimentally under specific environmental conditions. We found large and significant heritabilities (broad-sense) for all traits and several negative genetic correlations between traits (trade-offs), which are related to reproduction (i.e., numbers of the various sexual or asexual morphs) or dispersal (i.e., numbers of winged or wingless morphs). These results suggest that R. padi exhibits lineage specialization both in reproductive and dispersal strategies. In addition, we found important differences in the structure of genetic variance—covariance matrices (G) between phases. These differences were due to two large, negative genetic correlations detected during the asexual phase only: (1) between fecundity and age at maturity and (2) between the production of wingless and winged parthenogenetic females. We propose that this differential expression in genetic architecture results from a reallocation scheme during the asexual phase, when sexual morphs are not produced. We also found significant G × E interaction and nonsignificant genetic correlations across phases, indicating that genotypes could respond independently to selection in each phase. Our results reveal a rather unique situation in which the same population and even the same genotypes express different genetic (co)variation under different environmental conditions, driven by optimal resource allocation criteria.