Adverse genetic correlations between wood volume and quality traits are one of the main constraints in advancing radiata pine and other pine breeding programs. To overcome or deal with adverse genetic correlation in radiata pine and other conifer breeding programs, a Monte Carlo simulation study for the adversely correlated traits DBH and wood density was conducted using allele-based models. Two allelic models were generated for the study: a mixed-loci model using independent and pleiotropic loci (i.e. each locus affecting more than one trait) for adversely correlated traits and an all-antagonistic-pleiotropic-loci model. Selection was conducted for three scenarios: the first was based on a single trait, the second on index selection for two adversely correlated traits (DBH and wood density) with equal or, third, unequal economic weights. Results indicated that: 1. Adverse genetic correlation tends to increase under pleiotropic models with selection. 2. Genetic gains for adversely correlated traits (such as DBH and wood density) could be made for many generations with selective breeding if there are independent loci for individual traits. 3. New alleles (from infusion or mutation) with less antagonistic effect are required for further genetic gain in the two adversely correlated traits simultaneously if all independent alleles are fixed (i.e. without allelic variation) and pleiotropic loci with antagonistic effects are not fixed. 4. For short-term genetic gain in adversely correlated traits, selection based on two traits simultaneously is more effective than selection based on a single trait. Developing economic weights through breeding objectives is a sound approach for short-term breeding programs. Economic weights will influence genetic gain for individual traits and genetic correlation between traits. 5. For long-term genetic gain, dissecting the genetic basis of traits using a large association population is recommended. When the genetic mechanisms controlling adversely correlated traits are better understood, an allele model could be developed to study optimal strategies under different gene actions.