Background: The objective of the current study was to understand treatment preferences and their association with financial toxicity in Patient Advocate Foundation clients with breast cancer. Methods: This choice-based conjoint analysis used data from a nationwide sample of women with breast cancer who received assistance from the Patient Advocate Foundation. Choice sets created from 13 attributes of 3 levels each elicited patient preferences and trade-offs. Latent class analysis segmented respondents into distinct preference archetypes. The Comprehensive Score for Financial Toxicity (COST) tool captured financial toxicity. Adjusted generalized linear models estimated COST score differences by preference archetype. Results: Of 220 respondents (for a response rate of 10%), the median age was 58 years (interquartile range, 49-66 years); 28% of respondents were Black, indigenous, or people of color; and approximately 60% had household incomes <$40,000. The majority of respondents were diagnosed with early-stage cancer (91%), 38% had recurrent disease, and 61% were receiving treatment. Treatment choice was most affected by preferences related to affordability and impact on activities of daily living. Two distinct treatment preference archetypes emerged. The “cost-prioritizing group” (75% of respondents) was most concerned about affordability, impact on activities of daily living, and burdening care partners. The “functional independence–prioritizing group” (25% of respondents) was most concerned about their ability to work, physical side effects, and interference with life events. COST scores were found to be similar between the archetypes in adjusted models (cost-prioritizing group COST score, 12 [95% confidence interval, 9-14]; and functional independence–prioritizing COST score, 11 [95% confidence interval, 9-13]). Conclusions: Patients with breast cancer prioritized affordability or maintaining functional independence when making treatment decisions. Because of this variability, preference evaluation during treatment decision making could optimize patients' treatment experiences.