The　GSCP　(Gauss-Seidel　based　projection-contraction)　algorithm　has　been　well　established　in　solving　elastoplastic　mixed　complementarity　problems　(MiCPs)　or　other　problems　which　have　similar　mathematical　form.　However,　the　stability　problems　in　the　GSPC　algorithm　may　cause　nonconvergence　or　program　crashes.　Thus,　the　method　of　constructing　and　analyzing　counterexamples　is　adopted　to　study　the　reasons　for　the　instability　of　the　GSPC　algorithm.　The　R-GSPC　algorithm　was　developed　by　modifying　the　part　of　the　GSPC　algorithm　that　caused　the　crash　to　make　the　algorithm　stable.　In　addition,　this　paper　analyzes　the　convergence,　compatibility,　and　initial　value　impact　of　R-GSPC,　and　finds　that　the　R-GSPC　algorithm　greatly　improves　the　robustness　and　stability　without　losing　accuracy.　This　paper　allows　the　GSPC　algorithm　to　be　better　extended　to　more　fields　and　to　be　suitabled　for　being　introduced　into　commercial　programs.