An　improved　particle　swarm　optimization　(PSO)　algorithm　based　on　Gaussian　distribution　model　is　proposed　to　realize　the　autotuning　of　weighting　factors　for　the　cost　function　design　in　the　model　predictive　control　method.　First,　the　design　principle　of　the　weighting　factors　in　model　predictive　torque　control　for　permanent　magnet　synchronous　motor　system　is　analyzed.　Then,　using　the　root　mean　square　of　the　current　error　in　the　two-phase　rotating　coordinate　system　and　the　system　switching　frequency　as　references,　the　objective　function　of　the　particles　in　the　PSO　is　designed　by　considering　the　main　control　goals　of　reducing　the　torque　ripple,　the　current　total　harmonic　distortion,　and　the　switching　frequency.　The　Gaussian　individual　optimal　distribution　model　is　used　to　update　the　particle　position　on　the　structure　of　the　conventional　PSO　algorithm.　The　experimental　results　show　that　the　proposed　method　can　solve　the　problem　of　weighting　factors　design　as　it　reduces　the　switching　frequency　of　the　system　while　achieving　excellent　steady-state　performance.