The　failure　probability　of　a　system　can　be　expressed　as　an　integral　of　the　joint　probability　density　function　within　the　failure　domain　defined　by　the　limit　state　functions　of　the　system.　Generally,　it　is　very　difficult　to　solve　this　integral　directly.　The　evaluation　of　system　reliability　has　been　the　active　research　area　during　the　recent　decades.　Some　methods　were　developed　to　solve　system　reliability　analysis,　such　as　Monte　Carlo　method,　importance　sampling　method,　bounding　techniques　and　Probability　Network　Evaluation　Technique　(PNET).　This　paper　presents　the　implementation　of　several　optimization　algorithms,　modified　Method　of　Feasible　Direction　(MFD),　Sequential　Linear　Programming　(SLP)　and　Sequential　Quadratic　programming　(SQP),　in　order　to　demonstrate　the　convergence　abilities　and　robust　nature　of　the　optimization　technique　when　applied　to　series　system　reliability　analysis.　Examples　taken　from　the　published　references　were　calculated　and　the　results　were　compared　with　the　answers　of　various　other　methods　and　the　exact　solution.　Results　indicate　the　optimization　technique　has　a　wide　range　of　application　with　good　convergence　ability　and　robustness,　and　handle　problems　under　generalized　conditions　or　cases.