Nonlinear　model　predictive　control　has　been　used　in　motion　cueing　algorithms　recently　to　consider　the　nonlinear　dynamics　model　of　the　system.　The　entire　motion　cueing　algorithm　indexes,　including　the　physical　and　dynamical　constraints　of　the　actuators　and　physical　constraints　of　passive　joints,　can　be　controlled　with　precision　using　nonlinear　model　predictive　control.　However,　several　weighting　parameters　in　the　nonlinear　model　predictive　control-based　motion　cueing　algorithm　(including　driving　sensation,　motion　description　of　the　actuators,　and　passive　joints)　require　proper　and　laborious　tuning　to　attain　an　optimal　design　structure.　In　this　work,　the　optimal　weighting　parameters　of　a　nonlinear　predictive　control-based　motion　cueing　algorithm　model　are　calculated　using　cascade　optimisation　and　human　interaction.　A　cascade　optimisation　method　consisting　of　a　particle　swarm　optimisation　and　genetic　algorithm　is　designed　to　identify　the　best　weighting　parameters　compared　to　those　from　one　optimiser.　In　addition,　the　human　decision-making　units　are　added　to　the　two-level　cascade　optimiser　to　determine　the　best　solution　from　a　Pareto　front.　The　proposed　cascade　optimiser　decreases　the　run-time　with　better　extraction　of　the　optimal　weighting　parameters　to　increase　the　motion　fidelity　compared　to　a　single　optimiser.　It　should　be　noted　that　the　proposed　methodology　is　applied　along　longitudinal　channel.　While　the　same　methodology　can　be　applied　along　lateral,　heave　and　yaw　channels　for　further　evaluation　of　the　proposed　method.　The　proposed　model　is　simulated　utilising　the　MATLAB　software　and　the　results　prove　the　efficiency　of　the　newly　proposed　model　compared　to　those　from　the　previous　single　optimiser　in　reproducing　more　accurate　motion　signals　with　better　usage　of　the　driving　motion　platform　workspace.