As　a　new　paradigm,　industrial　Internet　provides　information　sharing　of　various　elements　and　resources　in　a　whole　industrial　production　process.　It　makes　industrial　production　processes　intelligent　and　provides　low-cost　and　efficient　scheduling.　Manufacturing　planning　for　multi-plant　enterprises　in　industrial　Internet　brings　many　big　challenges　due　to　numerous　optimization　variables　and　limits　of　manufacturing　capacities　of　plants,　production　resources,　etc.　Current　studies　fail　to　jointly　consider　the　cost　of　different　products　in　multiple　heterogeneous　plants,　and　ignore　machine-level　scheduling　of　manufacturing　tasks.　This　work　designs　an　improved　framework　for　multi-plant　enterprises,　based　on　which　a　constrained　non-linear　integer　program　for　reducing　the　total　cost　including　production　cost　and　transportation　one　is　formulated.　It　jointly　considers　many　complex　nonlinear　constraints,　e.g.,　limits　of　replacement　times,　storage　space,　substitution　and　pairing　production.　It　investigates　machine-level　task　scheduling　where　different　machines　have　heterogeneous　manufacturing　capacities.　To　solve　it,　this　work　proposes　an　algorithm　named　Genetic　Simulated　annealing-based　Particle　Swarm　Optimization　(GSPSO).　Realistic　data-based　experiments　demonstrate　GSPSO　reduces　the　cost　of　a　multi-plant　system　by　at　least　23%　than　its　typical　peers.　©　2022　IEEE.