Releasing electronic health records (EHRs) typically requires trialing multiple, complementary privacy models—k-anonymity, l-diversity, and t-closeness—before publication, with model and parameter choices selected to fit the release context by balancing each model’s privacy protection level against task-specific data utility. However, most methods optimize these models in isolation, limiting effectiveness and efficiency. We formalize their joint execution as multi-task optimization (MTO) over a shared anonymization plan spanning per-attribute generalization and record suppression. Further, we present multi – task anonymization by differential evolution (MTADE), a differential evolution (DE) framework that coevolves three privacy model related populations with distributed evaluation and an elite-migration/weak-replacement knowledge transfer policy, enabling cross-task reuse while curbing negative transfer. Across 16 healthcare datasets and six DE backbones, MTADE attains higher utility under equal privacy thresholds and converges faster than per-model single-task optimizers, yielding robust anonymization plans. This formulation and algorithm provide a principled route to simultaneously satisfying k-anonymity, l-diversity, and t-closeness for EHR release within a unified optimization pipeline, avoiding fragmented per-model tuning.