As　manufacturing　processes　continue　to　evolve,　distributed　and　energy-efficient　flexible　manufacturing　systems　have　become　the　central　paradigm　of　intelligent　manufacturing.　To　effectively　meet　the　demands　of　intricate　production　scheduling　requirements,　it　is　crucial　to　take　into　account　practical　factors,　such　as　two-stage　assembly　production　and　finite　transportation　resources.　However,　this　imposes　greater　demands　on　the　efficiency　of　scheduling　algorithms.　In　this　paper,　a　knowledge-based　bi-hierarchical　optimization　algorithm　(KBOA)　is　specially　designed　for　solving　the　distributed　energy-efficient　assembly　flexible　job　shop　problem　with　transportation　constraints　(DEAFJSP-T).　The　objectives　of　minimizing　makespan　and　total　energy　consumption　are　simultaneously　taken　into　account.　The　search　process　of　the　KBOA　is　structured　into　two　distinct　modes.　In　the　first　mode,　single-objective　searches　are　conducted　along　several　specified　directions.　To　elevate　the　overall　quality　of　the　population,　knowledge-based　initialization　strategies　are　employed,　tailored　for　different　objectives.　Furthermore,　the　knowledge-based　local　search　is　designed　to　strengthen　the　exploitation　of　the　KBOA　by　leveraging　the　interconnections　among　subproblems.　Incorporating　the　search　results　from　the　first　mode,　the　multi-objective　search　is　conducted　based　on　Pareto　dominance　in　the　second　stage.　Considering　the　characteristics　of　the　problem,　individual　cooperation　is　employed　to　enhance　the　search　efficiency　of　the　KBOA.　Extensive　experiments　are　conducted　to　assess　the　performances　of　the　KBOA.　The　statistical　comparisons　demonstrate　that　the　KBOA　is　superior　in　solving　the　DEAFJSP-T　in　terms　of　efficiency　and　effectiveness.